IBM Coursera Capstone Project¶

Table of Contents¶

Introduction: Business Problem
Data
Methodology and Results
Discussion
Conclusion

Introduction: Business Problem¶

In this project, we are going to compare the neighborhoods of the New York City in the US and the city of Toronto in Canada, and determine how similar they are in terms of businesses. We will analyze for both cities

what types of businesses are more likely to thrive;
what neighborhoods are suitable for each type of business;
what types of businesses are less desirable.

The results of the project will enable more effective decisions making for business people who want to start their business in these two big cities.

Data¶

Based on definition of our problem, following data sources will be needed to extract/generate the required information:

Neiborhood coordinates for New York from IBM Developer Skills Network
Neiborhood coordinates for Toronto from Wikipedia
Venue data for both New York and Toronto from FOURSQURE

Methodology and Results¶

Code in this section is based on DP0701EN-3-3-2-Neighborhoods-New-York-py-v2.0, which is part of a lab material in this course (Applied Data Science Capstone).

Importing relevant modules
Data for New York
Data for Toronto
Exploratory Data Analysis

0. Importing relevant modules and plotting configurations¶

import numpy as np
import pandas as pd


pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

import json # library to handle JSON files

# !pip install geopy
from geopy.geocoders import Nominatim # convert an address into latitude and longitude values
import requests 
from pandas.io.json import json_normalize # tranform JSON file into a pandas dataframe

# Matplotlib and associated plotting modules
import matplotlib.cm as cm
import matplotlib.colors as colors
from matplotlib import pyplot as plt

from sklearn.cluster import KMeans
import folium # map rendering library
from IPython.display import HTML
import time

print('Libraries imported.')

Libraries imported.

# plotting configurations that will be used later

%config InlineBackend.figure_format = 'retina'
plt.rc('figure', dpi=300)
plt.rc('savefig', dpi=300)
fig_size = (12,6)
big_fig_size = (18,8)
fig_fc = '#ffffff'
pc = ["#4285f4", "#db4437", "#f4b400", "#0f9d58", "#ab47bc", "#00acc1", "#ff7043", 
      "#9e9d24", "#5c6bc0", "#f06292", "#00796b", "#c2185b", "#7e57c2", "#03a9f4", 
      "#8bc34a", "#fdd835", "#fb8c00", "#8d6e63", "#9e9e9e", "#607d8b"]

def plot_conf(ax, xlbl='', ylbl='', t=''):
    """
    This function perform operations to produce better-looking 
    visualizations
    """
    # changing the background color of the plot
    ax.set_facecolor('#ffffff')
    # modifying the ticks on plot axes
    ax.tick_params(axis='both', labelcolor='#616161', color='#ffffff')
    ax.tick_params(axis='both', which='major', labelsize=9)
    # adding a grid and specifying its color
    ax.grid(True, color='#e9e9e9')
    # making the grid appear behind the graph elements
    ax.set_axisbelow(True)
    # hiding axes
    ax.spines['bottom'].set_color('#ffffff')
    ax.spines['top'].set_color('#ffffff') 
    ax.spines['right'].set_color('#ffffff')
    ax.spines['left'].set_color('#ffffff')
    # setting the title, x label, and y label of the plot
    ax.set_title(t, fontsize=14, color='#616161', loc='left', pad=24, fontweight='bold');
    ax.set_xlabel(xlbl, labelpad=16, fontsize=11, color='#616161', fontstyle='italic');
    ax.set_ylabel(ylbl, color='#616161', labelpad=16, fontsize=11, fontstyle='italic');
    
# table configuration
styles = [
    dict(selector="td, th", props=[("border", "1px solid #333"), ("padding", "2px")]),
    dict(selector="th.col_heading", props=[("background", "#eee8d5"), ("color", "#b58900"), ("padding", "5px 8px")]),
    dict(selector="th.index_name", props=[("background", "#eee8d5"), ("color", "#268bd2"), ("padding", "5px 8px")]),
    dict(selector="th.blank", props=[("background", "#eee8d5"), ("color", "#268bd2"), ("padding", "0")]),
    dict(selector="th.row_heading.level0", props=[("background", "rgba(133, 153, 0, 0.1)")]),
    dict(selector="th.row_heading.level1", props=[("background", "rgba(42, 161, 152, 0.1)")]),
    dict(selector="thead tr:nth-child(2) th", props=[("border-bottom", "3px solid #333333")]),
    dict(selector="td:hover", props=[("font-weight", "bold"), ("background", "#002b36"), ("color", "Gold")]),
]

disp_fmt = "<h2 style='color: #b58900'>{}<span style='color: #FF91CE; font-size: 115%'>:</span></h2>"

1. Data for New York City¶

1.1 Coordinates of neighborhoods in NYC¶

Downloading and saving neighborhood data of New York City¶

Once downloaded, we save the data locally so that we can do data experiments without repeatedly downloading the data.

# # We only need to do this once. 
# !wget -q -O './capstone_data/newyork_data.json' https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/labs/newyork_data.json
# print('Data downloaded!')

Loading the saved neighborhood JSON data for New York City¶

with open('./capstone_data/newyork_data.json') as json_data:
    newyork_data = json.load(json_data)
# newyork_data
nyc_neighborhoods_data = newyork_data['features']
nyc_neighborhoods_data[0]

{'type': 'Feature',
 'id': 'nyu_2451_34572.1',
 'geometry': {'type': 'Point',
  'coordinates': [-73.84720052054902, 40.89470517661]},
 'geometry_name': 'geom',
 'properties': {'name': 'Wakefield',
  'stacked': 1,
  'annoline1': 'Wakefield',
  'annoline2': None,
  'annoline3': None,
  'annoangle': 0.0,
  'borough': 'Bronx',
  'bbox': [-73.84720052054902,
   40.89470517661,
   -73.84720052054902,
   40.89470517661]}}

Saving the neighborhood data into a pandas dataframe¶

# define the dataframe columns
column_names = ['Borough', 'Neighborhood', 'Latitude', 'Longitude'] 

# instantiate the dataframe
nyc_neighborhoods = pd.DataFrame(columns=column_names)

for data in nyc_neighborhoods_data:
    borough             = data['properties']['borough'] 
    neighborhood_name   = data['properties']['name']
    
    neighborhood_latlon = data['geometry']['coordinates']
    neighborhood_lat = neighborhood_latlon[1]
    neighborhood_lon = neighborhood_latlon[0]
    
    nyc_neighborhoods = nyc_neighborhoods.append(
                        {'Borough'     : borough,
                         'Neighborhood': neighborhood_name,
                         'Latitude'    : neighborhood_lat,
                         'Longitude'   : neighborhood_lon}, 
                        ignore_index=True)

nyc_neighborhoods.head()

print('The dataframe has {} boroughs and {} neighborhoods.'.format(
        len(nyc_neighborhoods['Borough'].unique()),
        nyc_neighborhoods.shape[0])
)

The dataframe has 5 boroughs and 306 neighborhoods.

Transforming data further (needed later for the venue data)¶

In this dataframe, there are neighborhoods that share the same name but are located in different boroughs as shown below:

nnvc = nyc_neighborhoods['Neighborhood'].value_counts()
nnvc[nnvc > 1]

Sunnyside      2
Bay Terrace    2
Murray Hill    2
Chelsea        2
Name: Neighborhood, dtype: int64

nyc_neighborhoods[nyc_neighborhoods['Neighborhood'] == 'Bay Terrace']

To deal with this case, we will include the borough name in the name of these neighborhoods. For example, "Bay Terrace" neighborhood which is located in "Staten Island" borough will be named "Bay Terrace, Staten Island" and the one in "Queens" will be named "Bay Terrace, Queens":

for i in range(nyc_neighborhoods.shape[0]):
    nyn_ = nyc_neighborhoods.loc[i, 'Neighborhood']
    if nyc_neighborhoods[nyc_neighborhoods['Neighborhood'] == nyn_].shape[0] > 1:
        ind_ = nyc_neighborhoods[nyc_neighborhoods['Neighborhood'] == nyn_].index.tolist()
        for j in ind_:
            nyb__ = nyc_neighborhoods.loc[j, 'Borough']
            nyc_neighborhoods.loc[j, 'Neighborhood'] = nyn_ + ', ' + nyb__

nyc_neighborhoods[nyc_neighborhoods['Neighborhood'].str.startswith('Bay Terrace')]

Using `geopy` Library to get the Coordinates of New York City¶

address    = 'New York City, NY'
geolocator = Nominatim(user_agent="ny_explorer")

location   = geolocator.geocode(address)
latitude   = location.latitude
longitude  = location.longitude
print('The geograpical coordinate of New York City are {}, {}.'.format(latitude, longitude))

The geograpical coordinate of New York City are 40.7127281, -74.0060152.

Creating a Map of New York City with neighborhoods Superimposed on Top¶

nyc_borough_list = nyc_neighborhoods['Borough'].unique()
colors = {
    nyc_borough_list[0]:'red',
    nyc_borough_list[1]:'blue',
    nyc_borough_list[2]:'yellow',
    nyc_borough_list[3]:'green',
    nyc_borough_list[4]:'orange'}
print(colors)

# create map of New York using latitude and longitude values
map_newyork = folium.Map(location = [latitude, longitude], 
                         zoom_start = 10, 
                         min_zoom   = 9, 
                         max_zoom   = 11)

# add markers of neighborhoods to map
for lat, lng, borough, neighborhood in zip(nyc_neighborhoods['Latitude'], 
                                           nyc_neighborhoods['Longitude'], 
                                           nyc_neighborhoods['Borough'], 
                                           nyc_neighborhoods['Neighborhood']):
    label = '{}, {}'.format(neighborhood, borough)
    label = folium.Popup(label, parse_html=True)
    folium.CircleMarker(
        [lat, lng],
        radius = 3,
        popup  = label,
        weight = 2,
        color  = 'black',
        fill   = True,
        fill_color   = colors[borough],
        fill_opacity = 0.7,
        parse_html   = False
    ).add_to(map_newyork) 
    
map_newyork

{'Bronx': 'red', 'Manhattan': 'blue', 'Brooklyn': 'yellow', 'Queens': 'green', 'Staten Island': 'orange'}

1.2 NYC Venue Data (Latitude, Longitude, Category)¶

Downloading and saving Venue Data For New York City from Foursquare¶

# (We only need to do this ONCE.)
# # Foursquare API credentials
# CLIENT_ID     = '0MUGDYKOTRADEMKPQAGC04ERDG2QGPNSCBU0NJUF5TCWDZ3F' 
# CLIENT_SECRET = 'ABAH0NCFR5UCPDIYYT5332EWLB2KM1TWPWMH2VN5414UPSOL' 
# VERSION = '20201124'

# LIMIT = 100 # A default Foursquare API limit value

# print('Your credentails:')
# print('CLIENT_ID: ' + CLIENT_ID)
# print('CLIENT_SECRET:' + CLIENT_SECRET)

# def getNearbyVenues(names, latitudes, longitudes, radius=500, LIMIT=100):
#     """
#     A function that retrieves information about venues in each neighborhood.
#     It takes as input a list of the names of the neighborhoods, a list of 
#     their latitudes, and a list of their longitudes.
#     It returns a dataframe with information about each neighborhood and its venues.
#     """
    
#     venues_list=[]
#     for name, lat, lng in zip(names, latitudes, longitudes):
#         print('+', end='')
            
#         # create the API request URL
#         url = ('https://api.foursquare.com/v2/venues/search?'
#                '&client_id={}'
#                '&client_secret={}'
#                '&v={}'
#                '&ll={},{}&intent=browse&radius={}&limit={}'
#                .format(CLIENT_ID, 
#                        CLIENT_SECRET, 
#                        VERSION, 
#                        lat, lng, radius,LIMIT))
        
            
#         # make the GET request
#         results = None
#         while results is None:
#             try:
#                 results = requests.get(url).json()["response"]["venues"]
#             except:
#                 print('X', end='')
#                 results = None
        
#         # return only relevant information for each nearby venue
#         venues_list.append([(name, lat, lng, v['name'], v['location']['lat'], 
#                              v['location']['lng'], v['categories'][0]['name']) 
#                             for v in results if len(v['categories']) > 0])

#     nearby_venues = pd.DataFrame([item for venue_list in venues_list for item in venue_list])
#     nearby_venues.columns = ['Neighborhood', 'Neighborhood Latitude', 'Neighborhood Longitude', 
#                              'Venue', 'Venue Latitude', 'Venue Longitude', 'Venue Category']
    
#     return(nearby_venues)

# df_nyc_nbhd = getNearbyVenues(names      = nyc_neighborhoods['Neighborhood'],
#                              latitudes  = nyc_neighborhoods['Latitude'],
#                              longitudes = nyc_neighborhoods['Longitude']
#                             )
# df_nyc_nbhd.to_csv('./capstone_data/nyc_venues.csv',index=False)

Loading the saved data into a pandas dataframe¶

nyc_venues = pd.read_csv('./capstone_data/nyc_venues.csv')

# Removing some records 
nyc_venues = nyc_venues[~nyc_venues['Venue Category'].isin([
    'Building', 'Office', 'Bus Line', 'Bus Station', 'Bus Stop', 'Road'])]
print(nyc_venues.shape)
nyc_venues.head()

(23776, 7)

Let's check how many venues were returned for each neighborhood¶

nyc_venues.groupby('Neighborhood').size()

Neighborhood
Allerton                      84
Annadale                      77
Arden Heights                 69
Arlington                     72
Arrochar                      77
Arverne                       85
Astoria                       75
Astoria Heights               66
Auburndale                    63
Bath Beach                    86
Battery Park City             87
Bay Ridge                     87
Bay Terrace, Queens           81
Bay Terrace, Staten Island    78
Baychester                    82
Bayside                       84
Bayswater                     80
Bedford Park                  68
Bedford Stuyvesant            86
Beechhurst                    90
Bellaire                      79
Belle Harbor                  83
Bellerose                     80
Belmont                       62
Bensonhurst                   84
Bergen Beach                  79
Blissville                    77
Bloomfield                    73
Boerum Hill                   90
Borough Park                  35
Breezy Point                  72
Briarwood                     75
Brighton Beach                90
Broad Channel                 88
Broadway Junction             69
Bronxdale                     70
Brooklyn Heights              39
Brookville                    67
Brownsville                   85
Bulls Head                    73
Bushwick                      68
Butler Manor                  72
Cambria Heights               79
Canarsie                      68
Carnegie Hill                 86
Carroll Gardens               83
Castle Hill                   79
Castleton Corners             77
Central Harlem                85
Charleston                    83
Chelsea, Manhattan            84
Chelsea, Staten Island        82
Chinatown                     89
City Island                   87
City Line                     88
Civic Center                  71
Claremont Village             77
Clason Point                  56
Clifton                       71
Clinton                       80
Clinton Hill                  87
Co-op City                    74
Cobble Hill                   74
College Point                 86
Concord                       61
Concourse                     86
Concourse Village             79
Coney Island                  74
Corona                        92
Country Club                  74
Crown Heights                 88
Cypress Hills                 87
Ditmas Park                   91
Dongan Hills                  68
Douglaston                    84
Downtown                      99
Dumbo                         80
Dyker Heights                 57
East Elmhurst                 79
East Flatbush                 84
East Harlem                   78
East New York                 74
East Tremont                  89
East Village                  86
East Williamsburg             89
Eastchester                   80
Edenwald                      62
Edgemere                      78
Edgewater Park                82
Egbertville                   70
Elm Park                      70
Elmhurst                      68
Eltingville                   87
Emerson Hill                  68
Erasmus                       37
Far Rockaway                  83
Fieldston                     81
Financial District            66
Flatbush                      89
Flatiron                      73
Flatlands                     82
Floral Park                   83
Flushing                      89
Fordham                       79
Forest Hills                  31
Forest Hills Gardens          85
Fort Greene                   32
Fort Hamilton                 88
Fox Hills                     73
Fresh Meadows                 45
Fulton Ferry                  88
Georgetown                    93
Gerritsen Beach               83
Glen Oaks                     64
Glendale                      71
Gowanus                       72
Gramercy                      84
Graniteville                  71
Grant City                    79
Grasmere                      70
Gravesend                     85
Great Kills                   89
Greenpoint                    88
Greenridge                    62
Greenwich Village             88
Grymes Hill                   75
Hamilton Heights              85
Hammels                       88
Heartland Village             80
High  Bridge                  85
Highland Park                 61
Hillcrest                     89
Hollis                        85
Holliswood                    85
Homecrest                     89
Howard Beach                  89
Howland Hook                  80
Hudson Yards                  24
Huguenot                      75
Hunters Point                 91
Hunts Point                   62
Inwood                        93
Jackson Heights               84
Jamaica Center                82
Jamaica Estates               18
Jamaica Hills                 76
Kensington                    75
Kew Gardens                   75
Kew Gardens Hills             80
Kingsbridge                   40
Kingsbridge Heights           86
Laurelton                     80
Lefrak City                   84
Lenox Hill                    84
Lighthouse Hill               66
Lincoln Square                89
Lindenwood                    78
Little Italy                  80
Little Neck                   80
Long Island City              83
Longwood                      80
Lower East Side               78
Madison                       53
Malba                         61
Manhattan Beach               76
Manhattan Terrace             84
Manhattan Valley              10
Manhattanville                84
Manor Heights                 75
Marble Hill                   72
Marine Park                   76
Mariner's Harbor              79
Maspeth                       81
Melrose                       79
Middle Village                78
Midland Beach                 79
Midtown                       64
Midtown South                 84
Midwood                       77
Mill Basin                    75
Mill Island                   70
Morningside Heights           85
Morris Heights                83
Morris Park                   83
Morrisania                    82
Mott Haven                    80
Mount Eden                    87
Mount Hope                    78
Murray Hill, Manhattan        86
Murray Hill, Queens           95
Neponsit                      86
New Brighton                  76
New Dorp                      81
New Dorp Beach                80
New Lots                      80
New Springville               83
Noho                          83
North Corona                  83
North Riverdale               85
North Side                    90
Norwood                       89
Oakland Gardens               87
Oakwood                       70
Ocean Hill                    85
Ocean Parkway                 83
Old Town                      81
Olinville                     83
Ozone Park                    79
Paerdegat Basin               80
Park Hill                     69
Park Slope                    92
Parkchester                   87
Pelham Bay                    84
Pelham Gardens                79
Pelham Parkway                82
Pleasant Plains               80
Pomonok                       80
Port Ivory                    74
Port Morris                   73
Port Richmond                 66
Prince's Bay                  72
Prospect Heights              76
Prospect Lefferts Gardens     87
Prospect Park South           91
Queens Village                77
Queensboro Hill               82
Queensbridge                  76
Randall Manor                 79
Ravenswood                    90
Red Hook                      88
Rego Park                     82
Remsen Village                58
Richmond Hill                 83
Richmond Town                 75
Richmond Valley               93
Ridgewood                     84
Riverdale                     86
Rochdale                      84
Rockaway Beach                91
Rockaway Park                 25
Roosevelt Island              76
Rosebank                      81
Rosedale                      89
Rossville                     78
Roxbury                       87
Rugby                         87
Sandy Ground                  69
Schuylerville                 59
Sea Gate                      70
Sheepshead Bay                81
Shore Acres                   76
Silver Lake                   81
Soho                          83
Somerville                    77
Soundview                     81
South Beach                   86
South Jamaica                 76
South Ozone Park              75
South Side                    92
Springfield Gardens           64
Spuyten Duyvil                72
St. Albans                    86
St. George                    85
Stapleton                     76
Starrett City                 67
Steinway                      85
Stuyvesant Town               53
Sunnyside Gardens             83
Sunnyside, Queens             84
Sunnyside, Staten Island      89
Sunset Park                   85
Sutton Place                  80
Throgs Neck                   74
Todt Hill                     79
Tompkinsville                 87
Tottenville                   63
Travis                        75
Tribeca                       84
Tudor City                    84
Turtle Bay                    67
Unionport                     89
University Heights            91
Upper East Side               80
Upper West Side               83
Utopia                        67
Van Nest                      85
Vinegar Hill                  76
Wakefield                     87
Washington Heights            41
Weeksville                    89
West Brighton                 87
West Farms                    71
West Village                  89
Westchester Square            86
Westerleigh                   89
Whitestone                    69
Williamsbridge                89
Williamsburg                  85
Willowbrook                   71
Windsor Terrace               50
Wingate                       81
Woodhaven                     93
Woodlawn                      83
Woodrow                       80
Woodside                      92
Yorkville                     88
dtype: int64

nyc_neighborhoods['Neighborhood'].unique().shape[0], nyc_venues['Neighborhood'].unique().shape[0]

(306, 306)

# In case Foursquare does not return any venue for some neighborhoods.

nyc_excluded_neighborhoods = set(nyc_neighborhoods['Neighborhood']).difference(nyc_venues['Neighborhood'])
print(nyc_excluded_neighborhoods)

set()

Let's find out how many unique categories can be curated from all the returned venues¶

print('There are {} uniques categories.'.format(len(nyc_venues['Venue Category'].unique())))

There are 576 uniques categories.

Performing one-hot encoding on the `Venue Category` variable¶

# one-hot encoding
nyc_onehot = pd.get_dummies(nyc_venues[['Venue Category']], prefix="", prefix_sep="")

nyc_onehot['Neighborhood'] = nyc_venues['Neighborhood'] 

# move 'Neighborhood' column to the first column
fixed_columns = [nyc_onehot.columns[-1]] + list(nyc_onehot.columns[:-1])
nyc_onehot    = nyc_onehot[fixed_columns]

nyc_onehot.head()

nyc_onehot.shape

(23776, 577)

Grouping rows by neighborhood and taking the mean of the frequency of occurrence of each category for each neighborhood¶

nyc_grouped = nyc_onehot.groupby('Neighborhood').mean().reset_index()
nyc_grouped.head()

nyc_grouped.shape

(306, 577)

Let's print each neighborhood along with the top 5 most common venues

num_top_venues = 5

for hood in nyc_grouped['Neighborhood']:
    print("----"+hood+"----")
    temp = nyc_grouped[nyc_grouped['Neighborhood'] == hood].T.reset_index()
    temp.columns = ['venue','freq']
    temp = temp.iloc[1:]
    temp['freq'] = temp['freq'].astype(float)
    temp = temp.round({'freq': 2})
    print(temp.sort_values('freq', ascending=False).reset_index(drop=True).head(num_top_venues))
    print('\n')

----Allerton----
                venue  freq
0  Salon / Barbershop  0.07
1     Laundry Service  0.06
2         Pizza Place  0.04
3         Gas Station  0.04
4          Non-Profit  0.04


----Annadale----
                 venue  freq
0          Pizza Place  0.08
1   Salon / Barbershop  0.08
2  American Restaurant  0.04
3           Nail Salon  0.04
4        Tattoo Parlor  0.04


----Arden Heights----
                         venue  freq
0  Professional & Other Places  0.06
1             Dentist's Office  0.04
2              Doctor's Office  0.04
3                       Church  0.04
4                          Bar  0.03


----Arlington----
                                      venue  freq
0                                    Church  0.10
1                            Hardware Store  0.06
2  Residential Building (Apartment / Condo)  0.04
3               Professional & Other Places  0.04
4                        Salon / Barbershop  0.04


----Arrochar----
                venue  freq
0       Deli / Bodega  0.05
1          Food Truck  0.04
2         Pizza Place  0.04
3        Liquor Store  0.03
4  Chinese Restaurant  0.03


----Arverne----
                 venue  freq
0            Surf Spot  0.06
1                 Bank  0.05
2  Housing Development  0.05
3       Sandwich Place  0.04
4           Playground  0.04


----Astoria----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.16
1                                    Lounge  0.04
2                                    Bakery  0.04
3                             Deli / Bodega  0.04
4                 Latin American Restaurant  0.03


----Astoria Heights----
                   venue  freq
0        Laundry Service  0.08
1          Deli / Bodega  0.06
2             Playground  0.03
3         General Travel  0.03
4  General Entertainment  0.03


----Auburndale----
             venue  freq
0  Automotive Shop  0.16
1            Train  0.06
2       Nail Salon  0.05
3    Deli / Bodega  0.05
4  Laundry Service  0.03


----Bath Beach----
                                      venue  freq
0                             Deli / Bodega  0.07
1                               Pizza Place  0.05
2                        Salon / Barbershop  0.03
3  Residential Building (Apartment / Condo)  0.03
4                               Gas Station  0.03


----Battery Park City----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.09
1                             Boat or Ferry  0.08
2                                      Park  0.05
3                           Harbor / Marina  0.03
4                             Metro Station  0.03


----Bay Ridge----
                                      venue  freq
0                        Salon / Barbershop  0.06
1  Residential Building (Apartment / Condo)  0.06
2                                       Spa  0.05
3                            Cosmetics Shop  0.05
4                        Italian Restaurant  0.05


----Bay Terrace, Queens----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.16
1                           Doctor's Office  0.10
2                             Women's Store  0.05
3                                      Pool  0.05
4                         Elementary School  0.04


----Bay Terrace, Staten Island----
                 venue  freq
0   Italian Restaurant  0.05
1     Dentist's Office  0.04
2          Gas Station  0.04
3  Housing Development  0.04
4        Deli / Bodega  0.03


----Baychester----
                  venue  freq
0           Gas Station  0.07
1    Chinese Restaurant  0.04
2        Discount Store  0.02
3     Electronics Store  0.02
4  Gym / Fitness Center  0.02


----Bayside----
                venue  freq
0    Greek Restaurant  0.05
1  Salon / Barbershop  0.05
2     Doctor's Office  0.05
3                 Spa  0.04
4                Café  0.04


----Bayswater----
                venue  freq
0               Plane  0.09
1  Salon / Barbershop  0.04
2              Church  0.04
3         Gas Station  0.04
4     Laundry Service  0.04


----Bedford Park----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.18
1                             Deli / Bodega  0.12
2                                    Church  0.06
3                        Salon / Barbershop  0.04
4                   Fruit & Vegetable Store  0.03


----Bedford Stuyvesant----
                venue  freq
0       Deli / Bodega  0.07
1              School  0.05
2  Salon / Barbershop  0.03
3     Laundry Service  0.03
4              Church  0.03


----Beechhurst----
                  venue  freq
0    Salon / Barbershop  0.08
1                  Bank  0.04
2    Chinese Restaurant  0.04
3       Doctor's Office  0.04
4  Gym / Fitness Center  0.03


----Bellaire----
                venue  freq
0  Salon / Barbershop  0.06
1  Chinese Restaurant  0.06
2       Deli / Bodega  0.06
3         Pizza Place  0.05
4         Gas Station  0.04


----Belle Harbor----
                venue  freq
0               Beach  0.18
1            Boutique  0.05
2              School  0.05
3     Doctor's Office  0.04
4  Salon / Barbershop  0.04


----Bellerose----
                venue  freq
0    Dentist's Office  0.05
1         Pizza Place  0.04
2       Deli / Bodega  0.04
3  Salon / Barbershop  0.04
4     Automotive Shop  0.04


----Belmont----
                venue  freq
0  Salon / Barbershop  0.08
1  Italian Restaurant  0.06
2       Deli / Bodega  0.06
3              School  0.05
4                 Pub  0.03


----Bensonhurst----
                venue  freq
0     Doctor's Office  0.08
1          Nail Salon  0.06
2       Design Studio  0.04
3  Italian Restaurant  0.04
4                Park  0.04


----Bergen Beach----
                venue  freq
0     Doctor's Office  0.08
1          Playground  0.05
2  Salon / Barbershop  0.04
3    Dentist's Office  0.04
4         Event Space  0.04


----Blissville----
                                      venue  freq
0                               Gas Station  0.09
1                           Automotive Shop  0.06
2                             Deli / Bodega  0.06
3                            Hardware Store  0.06
4  Residential Building (Apartment / Condo)  0.04


----Bloomfield----
                  venue  freq
0       Doctor's Office  0.14
1        Baseball Field  0.05
2       Automotive Shop  0.05
3  Other Great Outdoors  0.04
4   Sporting Goods Shop  0.04


----Boerum Hill----
                                      venue  freq
0                        Salon / Barbershop  0.09
1                           Doctor's Office  0.04
2                                  Boutique  0.03
3                     General Entertainment  0.03
4  Residential Building (Apartment / Condo)  0.03


----Borough Park----
                       venue  freq
0                  Synagogue  0.17
1                       Bank  0.06
2             Clothing Store  0.06
3  College Academic Building  0.06
4            Doctor's Office  0.03


----Breezy Point----
       venue  freq
0      Beach  0.15
1        Bar  0.06
2     Church  0.04
3  Surf Spot  0.04
4      Trail  0.04


----Briarwood----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.15
1                             Deli / Bodega  0.05
2                                    Church  0.04
3                          Dentist's Office  0.04
4                                Playground  0.04


----Brighton Beach----
                     venue  freq
0       Salon / Barbershop  0.04
1  Fruit & Vegetable Store  0.03
2             Gourmet Shop  0.03
3  Health & Beauty Service  0.03
4        Mobile Phone Shop  0.03


----Broad Channel----
             venue  freq
0            Plane  0.08
1  Harbor / Marina  0.05
2           Church  0.03
3    Deli / Bodega  0.03
4             Park  0.03


----Broadway Junction----
                  venue  freq
0           High School  0.09
1         Metro Station  0.06
2  Other Great Outdoors  0.04
3         Deli / Bodega  0.04
4       Automotive Shop  0.04


----Bronxdale----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.10
1                           Doctor's Office  0.07
2                             Deli / Bodega  0.07
3                          Dentist's Office  0.04
4                          Fraternity House  0.03


----Brooklyn Heights----
                                      venue  freq
0                           Doctor's Office  0.21
1  Residential Building (Apartment / Condo)  0.08
2                             Women's Store  0.05
3                           Laundry Service  0.05
4                        Salon / Barbershop  0.05


----Brookville----
              venue  freq
0   Doctor's Office  0.06
1     Deli / Bodega  0.04
2              Park  0.04
3  Dentist's Office  0.03
4       Pizza Place  0.03


----Brownsville----
                                      venue  freq
0                                    Church  0.08
1  Residential Building (Apartment / Condo)  0.06
2                             Deli / Bodega  0.06
3                                    School  0.05
4                       Housing Development  0.05


----Bulls Head----
              venue  freq
0   Doctor's Office  0.11
1       Pizza Place  0.07
2              Bank  0.05
3  Dentist's Office  0.04
4            Church  0.04


----Bushwick----
                                      venue  freq
0                    Thrift / Vintage Store  0.07
1                               Coffee Shop  0.06
2  Residential Building (Apartment / Condo)  0.06
3                             Deli / Bodega  0.06
4                                       Bar  0.04


----Butler Manor----
                venue  freq
0  Italian Restaurant  0.08
1                Park  0.04
2              School  0.04
3      Baseball Field  0.04
4               Beach  0.04


----Cambria Heights----
                  venue  freq
0       Doctor's Office  0.10
1  Caribbean Restaurant  0.09
2    Salon / Barbershop  0.09
3        Cosmetics Shop  0.05
4                Lounge  0.04


----Canarsie----
                  venue  freq
0    Salon / Barbershop  0.13
1  Caribbean Restaurant  0.09
2    Chinese Restaurant  0.06
3         Deli / Bodega  0.04
4       Doctor's Office  0.04


----Carnegie Hill----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.10
1                               Event Space  0.05
2                        Salon / Barbershop  0.05
3                                       Spa  0.03
4                        Mexican Restaurant  0.03


----Carroll Gardens----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.06
1                                Food Truck  0.05
2                             Deli / Bodega  0.05
3                           Laundry Service  0.04
4                         Convenience Store  0.04


----Castle Hill----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.05
1                                    School  0.05
2                        Spanish Restaurant  0.04
3                            Medical Center  0.04
4                            Emergency Room  0.04


----Castleton Corners----
                venue  freq
0     Doctor's Office  0.13
1              Church  0.05
2       Deli / Bodega  0.04
3  Salon / Barbershop  0.04
4    Dentist's Office  0.04


----Central Harlem----
                                      venue  freq
0                        Salon / Barbershop  0.11
1  Residential Building (Apartment / Condo)  0.07
2                                  Mountain  0.05
3                      Gym / Fitness Center  0.04
4                            Cosmetics Shop  0.02


----Charleston----
                venue  freq
0     Doctor's Office  0.08
1  Salon / Barbershop  0.04
2       Big Box Store  0.02
3    Department Store  0.02
4       Shopping Mall  0.02


----Chelsea, Manhattan----
                   venue  freq
0            High School  0.06
1          Deli / Bodega  0.04
2                 Church  0.02
3  General Entertainment  0.02
4            Music Venue  0.02


----Chelsea, Staten Island----
                venue  freq
0  Salon / Barbershop  0.04
1     Automotive Shop  0.04
2      Ice Cream Shop  0.04
3  Italian Restaurant  0.02
4         Pet Service  0.02


----Chinatown----
                venue  freq
0  Chinese Restaurant  0.10
1              Bakery  0.04
2              Bridge  0.03
3                Park  0.03
4  Athletics & Sports  0.02


----City Island----
                                      venue  freq
0                           Harbor / Marina  0.07
1                            Ice Cream Shop  0.02
2  Residential Building (Apartment / Condo)  0.02
3                        Italian Restaurant  0.02
4                                  Pharmacy  0.02


----City Line----
                venue  freq
0  Salon / Barbershop  0.09
1          Nail Salon  0.06
2  Chinese Restaurant  0.05
3  Miscellaneous Shop  0.05
4   Convenience Store  0.03


----Civic Center----
                 venue  freq
0  Government Building  0.06
1      Coworking Space  0.06
2      Doctor's Office  0.06
3           Food Truck  0.04
4      Conference Room  0.03


----Claremont Village----
                venue  freq
0  Salon / Barbershop  0.06
1      Medical Center  0.06
2         Pizza Place  0.05
3       Deli / Bodega  0.05
4  Chinese Restaurant  0.05


----Clason Point----
                 venue  freq
0                 Park  0.16
1  Housing Development  0.09
2   Salon / Barbershop  0.05
3               Lounge  0.05
4      Automotive Shop  0.04


----Clifton----
                venue  freq
0         Gas Station  0.07
1         Pizza Place  0.04
2            Hospital  0.04
3     Automotive Shop  0.04
4  Salon / Barbershop  0.04


----Clinton----
                   venue  freq
0                Theater  0.08
1             Restaurant  0.06
2        Laundry Service  0.04
3           Concert Hall  0.04
4  General Entertainment  0.04


----Clinton Hill----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.06
1                              Liquor Store  0.05
2                             Deli / Bodega  0.05
3                        Miscellaneous Shop  0.05
4                                Restaurant  0.03


----Co-op City----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.08
1                                    School  0.07
2                      Other Great Outdoors  0.04
3                                    Church  0.04
4                      Fast Food Restaurant  0.03


----Cobble Hill----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.12
1                      Other Great Outdoors  0.07
2                            Medical Center  0.05
3                                      Taxi  0.05
4                                Playground  0.04


----College Point----
                venue  freq
0     Automotive Shop  0.09
1                Bank  0.06
2  Mexican Restaurant  0.06
3  Salon / Barbershop  0.06
4     Doctor's Office  0.03


----Concord----
                venue  freq
0     Doctor's Office  0.10
1       Deli / Bodega  0.05
2  Chinese Restaurant  0.03
3              Bakery  0.03
4     Automotive Shop  0.03


----Concourse----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.12
1                        Salon / Barbershop  0.07
2                             Deli / Bodega  0.06
3                               Pizza Place  0.06
4                                Nail Salon  0.05


----Concourse Village----
                                      venue  freq
0                             Deli / Bodega  0.10
1  Residential Building (Apartment / Condo)  0.08
2                        Salon / Barbershop  0.06
3                        Chinese Restaurant  0.05
4                                      Park  0.04


----Coney Island----
                                      venue  freq
0                             Deli / Bodega  0.05
1                       Housing Development  0.05
2  Residential Building (Apartment / Condo)  0.05
3                            Police Station  0.04
4                            Medical Center  0.04


----Corona----
                venue  freq
0  Salon / Barbershop  0.13
1       Deli / Bodega  0.07
2  Mexican Restaurant  0.07
3              Bakery  0.03
4     Doctor's Office  0.03


----Country Club----
            venue  freq
0     Coffee Shop  0.04
1   Deli / Bodega  0.04
2     Pizza Place  0.04
3  Scenic Lookout  0.03
4      Nail Salon  0.03


----Crown Heights----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.06
1                             Deli / Bodega  0.05
2                               Pizza Place  0.05
3                        Salon / Barbershop  0.05
4                                    Lounge  0.03


----Cypress Hills----
                       venue  freq
0         Salon / Barbershop  0.15
1                 Nail Salon  0.06
2              Deli / Bodega  0.06
3                Pizza Place  0.05
4  Latin American Restaurant  0.05


----Ditmas Park----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.21
1                             Deli / Bodega  0.07
2                        Salon / Barbershop  0.05
3                                    Church  0.04
4                                Nail Salon  0.04


----Dongan Hills----
                venue  freq
0       Deli / Bodega  0.12
1     Doctor's Office  0.10
2  Salon / Barbershop  0.06
3                 Bar  0.04
4              Church  0.04


----Douglaston----
                venue  freq
0  Salon / Barbershop  0.06
1  Italian Restaurant  0.05
2     Laundry Service  0.04
3         Pizza Place  0.04
4  Chinese Restaurant  0.04


----Downtown----
                                      venue  freq
0                                Shoe Store  0.05
1                      Gym / Fitness Center  0.04
2  Residential Building (Apartment / Condo)  0.03
3                       American Restaurant  0.03
4                                       Bar  0.02


----Dumbo----
                venue  freq
0         Art Gallery  0.10
1        Tech Startup  0.08
2          Food Stand  0.05
3  Salon / Barbershop  0.04
4          Food Truck  0.04


----Dyker Heights----
                venue  freq
0     Doctor's Office  0.12
1         Gas Station  0.07
2  Salon / Barbershop  0.07
3             Dog Run  0.05
4       Deli / Bodega  0.04


----East Elmhurst----
                venue  freq
0  Salon / Barbershop  0.11
1         Gas Station  0.05
2         Pizza Place  0.04
3          Playground  0.04
4               Plane  0.04


----East Flatbush----
                  venue  freq
0  Caribbean Restaurant  0.06
1    Salon / Barbershop  0.06
2         Deli / Bodega  0.06
3    Chinese Restaurant  0.05
4                Church  0.05


----East Harlem----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.08
1                            Clothing Store  0.04
2                               Pizza Place  0.04
3                                    Lawyer  0.03
4                                      Bank  0.03


----East New York----
                venue  freq
0  Salon / Barbershop  0.12
1       Deli / Bodega  0.09
2     Laundry Service  0.04
3              School  0.04
4  Spanish Restaurant  0.04


----East Tremont----
                venue  freq
0  Salon / Barbershop  0.13
1                Food  0.04
2      Cosmetics Shop  0.04
3          Shoe Store  0.03
4  Chinese Restaurant  0.03


----East Village----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.09
1                        Salon / Barbershop  0.07
2                       American Restaurant  0.03
3                        Miscellaneous Shop  0.03
4                            Clothing Store  0.02


----East Williamsburg----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.15
1                             Deli / Bodega  0.07
2                               Art Gallery  0.06
3                               Music Venue  0.06
4                         Convenience Store  0.04


----Eastchester----
                  venue  freq
0       Automotive Shop  0.26
1  Caribbean Restaurant  0.05
2         Deli / Bodega  0.04
3           Gas Station  0.04
4               Factory  0.04


----Edenwald----
                                      venue  freq
0                        Salon / Barbershop  0.10
1  Residential Building (Apartment / Condo)  0.10
2                              Liquor Store  0.05
3                                    Church  0.05
4                             Deli / Bodega  0.05


----Edgemere----
                                      venue  freq
0                            Medical Center  0.06
1  Residential Building (Apartment / Condo)  0.05
2                                   Dog Run  0.05
3                                     Beach  0.05
4                             Deli / Bodega  0.04


----Edgewater Park----
                venue  freq
0  Italian Restaurant  0.05
1       Deli / Bodega  0.05
2            Pharmacy  0.04
3  Chinese Restaurant  0.04
4      Medical Center  0.04


----Egbertville----
              venue  freq
0   Doctor's Office  0.10
1   Automotive Shop  0.04
2  Dentist's Office  0.04
3             Trail  0.04
4              Park  0.04


----Elm Park----
                venue  freq
0       Deli / Bodega  0.11
1         Pizza Place  0.04
2          Restaurant  0.04
3  Salon / Barbershop  0.04
4     Laundry Service  0.03


----Elmhurst----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.13
1                           Doctor's Office  0.09
2                           Thai Restaurant  0.07
3                           Bubble Tea Shop  0.06
4                              Optical Shop  0.04


----Eltingville----
                venue  freq
0  Salon / Barbershop  0.06
1          Nail Salon  0.05
2  Italian Restaurant  0.03
3     Automotive Shop  0.03
4             Butcher  0.03


----Emerson Hill----
                                      venue  freq
0                           Doctor's Office  0.31
1                            Medical Center  0.04
2                                    Church  0.04
3  Residential Building (Apartment / Condo)  0.04
4                           College Library  0.03


----Erasmus----
                   venue  freq
0     Salon / Barbershop  0.19
1                 Bakery  0.11
2   Caribbean Restaurant  0.11
3  General Entertainment  0.05
4                    Bar  0.05


----Far Rockaway----
                venue  freq
0  Salon / Barbershop  0.10
1       Deli / Bodega  0.06
2          Nail Salon  0.05
3     Automotive Shop  0.05
4  Chinese Restaurant  0.05


----Fieldston----
                                      venue  freq
0                                 Synagogue  0.06
1  Residential Building (Apartment / Condo)  0.05
2           College Administrative Building  0.05
3                              College Quad  0.04
4                 College Academic Building  0.04


----Financial District----
           venue  freq
0  Historic Site  0.06
1            Bar  0.05
2     Food Truck  0.05
3     Food Court  0.03
4    Pet Service  0.03


----Flatbush----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.18
1                        Salon / Barbershop  0.08
2                              Cocktail Bar  0.03
3                             Deli / Bodega  0.03
4                                    Lounge  0.02


----Flatiron----
                    venue  freq
0            Tech Startup  0.14
1          Clothing Store  0.05
2    Gym / Fitness Center  0.05
3     Sporting Goods Shop  0.05
4  Furniture / Home Store  0.04


----Flatlands----
                  venue  freq
0    Salon / Barbershop  0.10
1       Automotive Shop  0.07
2  Caribbean Restaurant  0.06
3              Pharmacy  0.05
4                Church  0.05


----Floral Park----
                venue  freq
0   Indian Restaurant  0.12
1     Doctor's Office  0.05
2              Lounge  0.05
3  Salon / Barbershop  0.05
4       Grocery Store  0.04


----Flushing----
                venue  freq
0                 Bar  0.09
1   Korean Restaurant  0.07
2         Karaoke Bar  0.07
3  Chinese Restaurant  0.04
4               Hotel  0.04


----Fordham----
             venue  freq
0   Clothing Store  0.08
1  Doctor's Office  0.05
2    Deli / Bodega  0.05
3             Bank  0.04
4       Nail Salon  0.04


----Forest Hills----
                                      venue  freq
0                          Dentist's Office  0.35
1                           Doctor's Office  0.23
2  Residential Building (Apartment / Condo)  0.19
3                                      Bank  0.03
4                              Tech Startup  0.03


----Forest Hills Gardens----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.16
1                        Salon / Barbershop  0.06
2                                      Park  0.05
3                                    Garden  0.04
4                                    Church  0.04


----Fort Greene----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.12
1                             Deli / Bodega  0.09
2                               Art Gallery  0.06
3                  Bike Rental / Bike Share  0.03
4                                Taco Place  0.03


----Fort Hamilton----
                                      venue  freq
0                           Doctor's Office  0.12
1  Residential Building (Apartment / Condo)  0.07
2                               Pizza Place  0.06
3                           Laundry Service  0.05
4                        Salon / Barbershop  0.05


----Fox Hills----
                                      venue  freq
0               Professional & Other Places  0.05
1                        Chinese Restaurant  0.05
2  Residential Building (Apartment / Condo)  0.05
3                       Housing Development  0.04
4                                Non-Profit  0.03


----Fresh Meadows----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.11
1                                    School  0.07
2                        Chinese Restaurant  0.04
3                           Laundry Service  0.04
4                        Salon / Barbershop  0.04


----Fulton Ferry----
                 venue  freq
0           Food Truck  0.09
1          Pizza Place  0.07
2  American Restaurant  0.06
3                 Park  0.06
4        Boat or Ferry  0.05


----Georgetown----
                venue  freq
0     Doctor's Office  0.12
1                Bank  0.06
2      Medical Center  0.03
3  Chinese Restaurant  0.03
4          Kids Store  0.02


----Gerritsen Beach----
                 venue  freq
0                  Bar  0.05
1          Gas Station  0.05
2  Housing Development  0.05
3            Bike Shop  0.04
4       Ice Cream Shop  0.04


----Glen Oaks----
                                      venue  freq
0                                  Hospital  0.08
1                           Laundry Service  0.05
2                                      Bank  0.05
3                                Playground  0.05
4  Residential Building (Apartment / Condo)  0.03


----Glendale----
                venue  freq
0     Doctor's Office  0.13
1       Deli / Bodega  0.08
2  Salon / Barbershop  0.04
3              Church  0.04
4  Chinese Restaurant  0.04


----Gowanus----
             venue  freq
0      Art Gallery  0.08
1    Design Studio  0.07
2  Coworking Space  0.06
3    Metro Station  0.04
4     Tech Startup  0.04


----Gramercy----
                                      venue  freq
0                           Doctor's Office  0.15
1  Residential Building (Apartment / Condo)  0.11
2                            Medical Center  0.06
3                          Dentist's Office  0.05
4                        Salon / Barbershop  0.05


----Graniteville----
               venue  freq
0         Nail Salon  0.04
1   Dentist's Office  0.04
2    Doctor's Office  0.04
3    Automotive Shop  0.03
4  Elementary School  0.03


----Grant City----
                  venue  freq
0       Doctor's Office  0.06
1      Asian Restaurant  0.05
2        Cosmetics Shop  0.05
3            Nail Salon  0.04
4  Fast Food Restaurant  0.04


----Grasmere----
                                      venue  freq
0                           Doctor's Office  0.06
1  Residential Building (Apartment / Condo)  0.04
2                        Chinese Restaurant  0.03
3                                      Food  0.03
4                               Pizza Place  0.03


----Gravesend----
                    venue  freq
0           Deli / Bodega  0.06
1         Automotive Shop  0.06
2             Pizza Place  0.04
3  Furniture / Home Store  0.04
4      Salon / Barbershop  0.04


----Great Kills----
                venue  freq
0  Salon / Barbershop  0.10
1     Doctor's Office  0.06
2          Nail Salon  0.04
3         Pizza Place  0.04
4  Italian Restaurant  0.03


----Greenpoint----
                                      venue  freq
0                           Doctor's Office  0.05
1                             Deli / Bodega  0.05
2                                   Butcher  0.05
3  Residential Building (Apartment / Condo)  0.03
4                               Medical Lab  0.02


----Greenridge----
                         venue  freq
0              Doctor's Office  0.16
1             Dentist's Office  0.05
2  Professional & Other Places  0.05
3                      Parking  0.03
4              Laundry Service  0.03


----Greenwich Village----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.09
1                                      Taxi  0.09
2                               Art Gallery  0.07
3                        Salon / Barbershop  0.05
4                            Clothing Store  0.03


----Grymes Hill----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.08
1                                    Church  0.05
2           College Administrative Building  0.05
3                      College & University  0.04
4                                   Dog Run  0.04


----Hamilton Heights----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.13
1                        Salon / Barbershop  0.09
2                             Deli / Bodega  0.05
3                                       Bar  0.04
4                           Laundry Service  0.04


----Hammels----
                                      venue  freq
0                                     Beach  0.17
1  Residential Building (Apartment / Condo)  0.09
2                                    Church  0.07
3                                    Bakery  0.02
4                           Automotive Shop  0.02


----Heartland Village----
                         venue  freq
0              Doctor's Office  0.10
1             Dentist's Office  0.04
2                   Bagel Shop  0.04
3  Professional & Other Places  0.04
4           Salon / Barbershop  0.04


----High  Bridge----
                                      venue  freq
0                        Salon / Barbershop  0.08
1  Residential Building (Apartment / Condo)  0.07
2                             Deli / Bodega  0.06
3                                  Pharmacy  0.06
4                           Laundry Service  0.05


----Highland Park----
                venue  freq
0  Salon / Barbershop  0.11
1              Church  0.08
2       Moving Target  0.05
3     Laundry Service  0.05
4        Liquor Store  0.05


----Hillcrest----
                          venue  freq
0     College Academic Building  0.06
1  General College & University  0.04
2                Student Center  0.03
3             College Classroom  0.03
4             College Cafeteria  0.03


----Hollis----
                  venue  freq
0    Salon / Barbershop  0.11
1       Automotive Shop  0.05
2  Caribbean Restaurant  0.05
3                Church  0.04
4       Laundry Service  0.04


----Holliswood----
                                      venue  freq
0                           Doctor's Office  0.09
1  Residential Building (Apartment / Condo)  0.06
2                               Gas Station  0.06
3                          Dentist's Office  0.05
4                        Salon / Barbershop  0.05


----Homecrest----
                         venue  freq
0           Salon / Barbershop  0.10
1           Chinese Restaurant  0.08
2             Asian Restaurant  0.04
3                       Bakery  0.03
4  Eastern European Restaurant  0.03


----Howard Beach----
                venue  freq
0  Salon / Barbershop  0.07
1  Italian Restaurant  0.06
2            Pharmacy  0.03
3                Café  0.02
4                 Gym  0.02


----Howland Hook----
             venue  freq
0    Boat or Ferry  0.08
1             Food  0.06
2          Factory  0.05
3              Bar  0.04
4  Automotive Shop  0.04


----Hudson Yards----
                    venue  freq
0  Furniture / Home Store  0.12
1          Medical Center  0.12
2              Food Truck  0.08
3              Bagel Shop  0.04
4              Donut Shop  0.04


----Huguenot----
                  venue  freq
0       Doctor's Office  0.05
1    Salon / Barbershop  0.05
2        Ice Cream Shop  0.04
3         Deli / Bodega  0.04
4  Other Great Outdoors  0.04


----Hunters Point----
                venue  freq
0       Deli / Bodega  0.05
1  Salon / Barbershop  0.04
2               Plaza  0.03
3         Yoga Studio  0.02
4                 Spa  0.02


----Hunts Point----
             venue  freq
0  Automotive Shop  0.16
1          Factory  0.08
2           School  0.05
3             Food  0.05
4      Pizza Place  0.05


----Inwood----
                 venue  freq
0        Deli / Bodega  0.05
1   Salon / Barbershop  0.05
2      Laundry Service  0.04
3           Nail Salon  0.04
4  Government Building  0.04


----Jackson Heights----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.18
1                          Dentist's Office  0.07
2                           Doctor's Office  0.06
3                 Latin American Restaurant  0.04
4                                   Parking  0.04


----Jamaica Center----
                 venue  freq
0     Department Store  0.05
1       Clothing Store  0.05
2   Salon / Barbershop  0.05
3  Government Building  0.04
4           Kids Store  0.04


----Jamaica Estates----
                          venue  freq
0                        Church  0.11
1                        School  0.11
2               Coworking Space  0.06
3  General College & University  0.06
4                   High School  0.06


----Jamaica Hills----
                                      venue  freq
0                        Salon / Barbershop  0.08
1                             Grocery Store  0.07
2                         Indian Restaurant  0.07
3  Residential Building (Apartment / Condo)  0.05
4                          Dentist's Office  0.04


----Kensington----
                venue  freq
0       Grocery Store  0.07
1                Bank  0.05
2     Doctor's Office  0.04
3  Mexican Restaurant  0.04
4          Food Truck  0.04


----Kew Gardens----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.08
1                           Doctor's Office  0.08
2                           Laundry Service  0.04
3                                      Taxi  0.04
4                                 Synagogue  0.04


----Kew Gardens Hills----
               venue  freq
0    Doctor's Office  0.05
1   Dentist's Office  0.05
2               Bank  0.05
3             School  0.04
4  Convenience Store  0.04


----Kingsbridge----
                venue  freq
0     Laundry Service  0.08
1                Bank  0.05
2  Salon / Barbershop  0.05
3            Boutique  0.05
4          Print Shop  0.02


----Kingsbridge Heights----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.10
1                        Salon / Barbershop  0.07
2                               Pizza Place  0.06
3                             Deli / Bodega  0.05
4                        Mexican Restaurant  0.05


----Laurelton----
                  venue  freq
0  Caribbean Restaurant  0.06
1    Salon / Barbershop  0.06
2           Gas Station  0.05
3                Church  0.05
4         Deli / Bodega  0.04


----Lefrak City----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.20
1                             Deli / Bodega  0.07
2                           Laundry Service  0.07
3                        Salon / Barbershop  0.05
4                           Cultural Center  0.05


----Lenox Hill----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.11
1                        Salon / Barbershop  0.06
2                                Nail Salon  0.04
3                               Art Gallery  0.04
4                           Doctor's Office  0.04


----Lighthouse Hill----
                venue  freq
0      History Museum  0.06
1  Italian Restaurant  0.06
2              Church  0.05
3         Gas Station  0.03
4                Park  0.03


----Lincoln Square----
                                      venue  freq
0                                   Theater  0.08
1                     Performing Arts Venue  0.06
2                               High School  0.06
3                               Opera House  0.04
4  Residential Building (Apartment / Condo)  0.04


----Lindenwood----
                                      venue  freq
0                           Doctor's Office  0.17
1                          Dentist's Office  0.08
2  Residential Building (Apartment / Condo)  0.06
3                                      Bank  0.05
4                             Deli / Bodega  0.04


----Little Italy----
                venue  freq
0  Italian Restaurant  0.28
1  Salon / Barbershop  0.05
2          Smoke Shop  0.04
3           Gift Shop  0.04
4        Gourmet Shop  0.04


----Little Neck----
                venue  freq
0  Chinese Restaurant  0.05
1  Salon / Barbershop  0.05
2              School  0.04
3   Korean Restaurant  0.04
4                Food  0.04


----Long Island City----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.10
1                             Deli / Bodega  0.06
2                                      Café  0.04
3                            Sandwich Place  0.04
4                               Pizza Place  0.04


----Longwood----
                venue  freq
0         Pizza Place  0.08
1     Automotive Shop  0.06
2               Train  0.06
3  Mexican Restaurant  0.05
4       Grocery Store  0.04


----Lower East Side----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.19
1                             Deli / Bodega  0.06
2                       Housing Development  0.06
3                  Bike Rental / Bike Share  0.04
4                               Art Gallery  0.04


----Madison----
                                      venue  freq
0                           Doctor's Office  0.13
1  Residential Building (Apartment / Condo)  0.09
2                                 Synagogue  0.09
3                          Dentist's Office  0.08
4                               High School  0.04


----Malba----
                venue  freq
0                Park  0.07
1           Rock Club  0.03
2          Food Truck  0.03
3  Chinese Restaurant  0.03
4          Nail Salon  0.03


----Manhattan Beach----
                venue  freq
0     Harbor / Marina  0.12
1       Boat or Ferry  0.12
2        Dessert Shop  0.04
3  Turkish Restaurant  0.04
4             Dog Run  0.03


----Manhattan Terrace----
                                      venue  freq
0                           Doctor's Office  0.12
1  Residential Building (Apartment / Condo)  0.07
2                          Dentist's Office  0.07
3                       Housing Development  0.05
4                                   Parking  0.05


----Manhattan Valley----
                   venue  freq
0     Salon / Barbershop   0.2
1          Deli / Bodega   0.2
2           Trailer Park   0.1
3        Laundry Service   0.1
4  Check Cashing Service   0.1


----Manhattanville----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.10
1           College Administrative Building  0.05
2              General College & University  0.04
3                        Salon / Barbershop  0.04
4                                University  0.04


----Manor Heights----
                venue  freq
0     Laundry Service  0.04
1     Doctor's Office  0.04
2          Donut Shop  0.04
3        Liquor Store  0.04
4  Miscellaneous Shop  0.03


----Marble Hill----
                                      venue  freq
0                             Deli / Bodega  0.08
1                        Salon / Barbershop  0.07
2                               High School  0.06
3  Residential Building (Apartment / Condo)  0.04
4                                    Church  0.04


----Marine Park----
                 venue  freq
0                 Park  0.05
1      Laundry Service  0.04
2           Nail Salon  0.04
3      Doctor's Office  0.04
4  American Restaurant  0.04


----Mariner's Harbor----
                         venue  freq
0              Automotive Shop  0.08
1                       Church  0.06
2                Deli / Bodega  0.05
3           Miscellaneous Shop  0.04
4  Professional & Other Places  0.04


----Maspeth----
                venue  freq
0         Pizza Place  0.09
1          Nail Salon  0.05
2    Dentist's Office  0.04
3  Salon / Barbershop  0.04
4                Bank  0.04


----Melrose----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.10
1                        Salon / Barbershop  0.06
2                           Doctor's Office  0.06
3                           Laundry Service  0.06
4                               High School  0.05


----Middle Village----
                venue  freq
0              Bakery  0.06
1  Chinese Restaurant  0.05
2      Cosmetics Shop  0.05
3  Salon / Barbershop  0.04
4  Miscellaneous Shop  0.03


----Midland Beach----
            venue  freq
0  Baseball Field  0.09
1  General Travel  0.08
2           Beach  0.05
3       Pet Store  0.05
4      Non-Profit  0.05


----Midtown----
                             venue  freq
0     General College & University  0.11
1  College Administrative Building  0.05
2                       Shoe Store  0.05
3              American Restaurant  0.03
4                           Mosque  0.03


----Midtown South----
                 venue  freq
0                 Taxi  0.06
1           Food Stand  0.05
2  American Restaurant  0.05
3                  Spa  0.04
4                Hotel  0.04


----Midwood----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.09
1                           Doctor's Office  0.05
2                                    Bakery  0.04
3                                 Synagogue  0.04
4                          Dentist's Office  0.03


----Mill Basin----
                venue  freq
0    Dentist's Office  0.08
1     Doctor's Office  0.05
2  Salon / Barbershop  0.05
3         Gas Station  0.05
4     Laundry Service  0.04


----Mill Island----
                       venue  freq
0            Harbor / Marina  0.04
1                       Pool  0.04
2  Middle Eastern Restaurant  0.03
3             Hardware Store  0.03
4                Bridal Shop  0.03


----Morningside Heights----
                       venue  freq
0                 Food Truck  0.18
1     College Residence Hall  0.07
2  College Academic Building  0.06
3             Student Center  0.05
4                Coffee Shop  0.05


----Morris Heights----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.11
1                             Grocery Store  0.06
2                         Convenience Store  0.04
3                        Salon / Barbershop  0.04
4                             Deli / Bodega  0.04


----Morris Park----
                                      venue  freq
0                           Doctor's Office  0.14
1  Residential Building (Apartment / Condo)  0.05
2                          Dentist's Office  0.04
3                                Nail Salon  0.04
4                               Pizza Place  0.02


----Morrisania----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.06
1                                    Church  0.06
2                               High School  0.06
3                        Chinese Restaurant  0.05
4                             Deli / Bodega  0.05


----Mott Haven----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.19
1                        Salon / Barbershop  0.06
2                        Chinese Restaurant  0.05
3                             Grocery Store  0.05
4                                    Church  0.04


----Mount Eden----
                                      venue  freq
0                           Automotive Shop  0.13
1  Residential Building (Apartment / Condo)  0.07
2                        Spanish Restaurant  0.05
3                               Pizza Place  0.05
4                           Doctor's Office  0.03


----Mount Hope----
                                      venue  freq
0                        Salon / Barbershop  0.15
1  Residential Building (Apartment / Condo)  0.08
2                                    Church  0.06
3                           Automotive Shop  0.05
4                             Deli / Bodega  0.04


----Murray Hill, Manhattan----
                                      venue  freq
0                           Doctor's Office  0.15
1  Residential Building (Apartment / Condo)  0.12
2                          Dentist's Office  0.09
3                                       Spa  0.03
4                               Event Space  0.03


----Murray Hill, Queens----
                venue  freq
0   Korean Restaurant  0.32
1              Bakery  0.04
2     Doctor's Office  0.04
3     Laundry Service  0.03
4  Salon / Barbershop  0.03


----Neponsit----
      venue  freq
0     Beach  0.22
1    School  0.05
2      Park  0.03
3  Boutique  0.03
4    Garden  0.02


----New Brighton----
             venue  freq
0    Deli / Bodega  0.14
1           Church  0.08
2             Park  0.07
3       Playground  0.05
4  Laundry Service  0.04


----New Dorp----
                venue  freq
0  Salon / Barbershop  0.09
1  Italian Restaurant  0.05
2  Mexican Restaurant  0.05
3           Pet Store  0.04
4              Bakery  0.04


----New Dorp Beach----
                   venue  freq
0     Italian Restaurant  0.04
1          Deli / Bodega  0.04
2  General Entertainment  0.04
3     Salon / Barbershop  0.04
4         Scenic Lookout  0.04


----New Lots----
                venue  freq
0              Church  0.09
1  Salon / Barbershop  0.08
2       Deli / Bodega  0.05
3  Chinese Restaurant  0.04
4     Laundry Service  0.04


----New Springville----
                venue  freq
0     Doctor's Office  0.11
1       Deli / Bodega  0.04
2  Salon / Barbershop  0.04
3          Bagel Shop  0.04
4   Mobile Phone Shop  0.04


----Noho----
                                      venue  freq
0                                       Bar  0.10
1  Residential Building (Apartment / Condo)  0.08
2                             Deli / Bodega  0.04
3                        Salon / Barbershop  0.04
4                              Cocktail Bar  0.04


----North Corona----
                       venue  freq
0              Deli / Bodega  0.07
1         Salon / Barbershop  0.05
2            Laundry Service  0.04
3  Latin American Restaurant  0.04
4         Mexican Restaurant  0.04


----North Riverdale----
                venue  freq
0   Mobile Phone Shop  0.05
1  Salon / Barbershop  0.05
2         Pizza Place  0.04
3                Bank  0.04
4                 Gym  0.04


----North Side----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.09
1                                       Bar  0.06
2                                 Nightclub  0.03
3                            Clothing Store  0.03
4                                  Wine Bar  0.03


----Norwood----
                                      venue  freq
0                           Doctor's Office  0.15
1                                      Park  0.07
2  Residential Building (Apartment / Condo)  0.06
3                             Deli / Bodega  0.06
4                            Medical Center  0.04


----Oakland Gardens----
                venue  freq
0   Korean Restaurant  0.08
1  Chinese Restaurant  0.06
2    Dentist's Office  0.05
3     Laundry Service  0.03
4    Sushi Restaurant  0.03


----Oakwood----
              venue  freq
0   Doctor's Office  0.24
1  Dentist's Office  0.10
2            Church  0.07
3    Medical Center  0.06
4   Other Nightlife  0.03


----Ocean Hill----
                                      venue  freq
0                             Deli / Bodega  0.11
1  Residential Building (Apartment / Condo)  0.09
2                        Salon / Barbershop  0.07
3                                    Church  0.05
4                             Metro Station  0.04


----Ocean Parkway----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.11
1                           Doctor's Office  0.08
2                           Coworking Space  0.04
3                          Dentist's Office  0.04
4                                 Synagogue  0.04


----Old Town----
                venue  freq
0      Medical Center  0.06
1                Bank  0.05
2  Italian Restaurant  0.05
3              Bakery  0.05
4      Cosmetics Shop  0.05


----Olinville----
                                      venue  freq
0                        Salon / Barbershop  0.13
1                             Deli / Bodega  0.06
2                      Caribbean Restaurant  0.06
3  Residential Building (Apartment / Condo)  0.05
4                                      Food  0.05


----Ozone Park----
           venue  freq
0  Deli / Bodega  0.09
1    Pizza Place  0.06
2       Pharmacy  0.05
3   Liquor Store  0.05
4     Nail Salon  0.04


----Paerdegat Basin----
                  venue  freq
0       Doctor's Office  0.09
1    Salon / Barbershop  0.06
2       Harbor / Marina  0.05
3  Caribbean Restaurant  0.04
4              Cemetery  0.04


----Park Hill----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.09
1                           Doctor's Office  0.09
2                               Event Space  0.03
3                       Housing Development  0.03
4                        Salon / Barbershop  0.03


----Park Slope----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.08
1                           Laundry Service  0.04
2                             Deli / Bodega  0.04
3                                Nail Salon  0.04
4                          Toy / Game Store  0.04


----Parkchester----
                                      venue  freq
0                        Salon / Barbershop  0.09
1                           Laundry Service  0.07
2  Residential Building (Apartment / Condo)  0.07
3                         Indian Restaurant  0.05
4                         Convenience Store  0.05


----Pelham Bay----
                                      venue  freq
0                            Medical Center  0.06
1                        Salon / Barbershop  0.06
2                        Chinese Restaurant  0.05
3                         Convenience Store  0.05
4  Residential Building (Apartment / Condo)  0.05


----Pelham Gardens----
                  venue  freq
0       Doctor's Office  0.20
1  Other Great Outdoors  0.04
2              Pharmacy  0.04
3            Playground  0.04
4           Pizza Place  0.04


----Pelham Parkway----
                                      venue  freq
0                           Doctor's Office  0.10
1                             Deli / Bodega  0.06
2                          Dentist's Office  0.06
3  Residential Building (Apartment / Condo)  0.06
4                                     Diner  0.04


----Pleasant Plains----
                venue  freq
0  Salon / Barbershop  0.12
1         Pizza Place  0.08
2              Bakery  0.04
3        Dessert Shop  0.02
4              Bridge  0.02


----Pomonok----
                                      venue  freq
0                           Doctor's Office  0.10
1  Residential Building (Apartment / Condo)  0.09
2                                Playground  0.06
3                          Dentist's Office  0.04
4                        Salon / Barbershop  0.04


----Port Ivory----
                                      venue  freq
0                             Boat or Ferry  0.07
1                           Automotive Shop  0.05
2                                Playground  0.04
3                                      Park  0.04
4  Residential Building (Apartment / Condo)  0.03


----Port Morris----
                                      venue  freq
0                                   Factory  0.10
1  Residential Building (Apartment / Condo)  0.07
2                       Government Building  0.04
3                             Design Studio  0.03
4                          Storage Facility  0.03


----Port Richmond----
                venue  freq
0     Automotive Shop  0.12
1       Deli / Bodega  0.09
2         Pizza Place  0.06
3  Mexican Restaurant  0.05
4     Laundry Service  0.05


----Prince's Bay----
                venue  freq
0  Salon / Barbershop  0.07
1     Doctor's Office  0.06
2              Church  0.06
3    Dentist's Office  0.04
4                Bank  0.04


----Prospect Heights----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.13
1                        Mexican Restaurant  0.05
2                             Deli / Bodega  0.05
3                           Thai Restaurant  0.04
4                        Salon / Barbershop  0.04


----Prospect Lefferts Gardens----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.11
1                        Salon / Barbershop  0.09
2                           Doctor's Office  0.06
3                             Deli / Bodega  0.06
4                                    School  0.05


----Prospect Park South----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.25
1                             Deli / Bodega  0.07
2                                    Church  0.04
3                        Salon / Barbershop  0.04
4                        Chinese Restaurant  0.03


----Queens Village----
                        venue  freq
0  Financial or Legal Service  0.06
1                        Food  0.06
2        Caribbean Restaurant  0.05
3                      Church  0.04
4                  Nail Salon  0.04


----Queensboro Hill----
                venue  freq
0  Chinese Restaurant  0.10
1     Doctor's Office  0.07
2    Asian Restaurant  0.06
3            Hospital  0.05
4      Medical Center  0.05


----Queensbridge----
                venue  freq
0               Hotel  0.08
1       Deli / Bodega  0.07
2  Miscellaneous Shop  0.07
3    Storage Facility  0.04
4       Metro Station  0.03


----Randall Manor----
                                      venue  freq
0                           Doctor's Office  0.10
1                               High School  0.09
2  Residential Building (Apartment / Condo)  0.08
3                                      Park  0.04
4                         College Classroom  0.04


----Ravenswood----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.14
1                           Laundry Service  0.08
2                                  Pharmacy  0.04
3                           Doctor's Office  0.03
4                             Grocery Store  0.03


----Red Hook----
                    venue  freq
0             Art Gallery  0.10
1  Furniture / Home Store  0.05
2               Bike Shop  0.03
3               Wine Shop  0.03
4                     Bar  0.03


----Rego Park----
                                      venue  freq
0                           Doctor's Office  0.15
1  Residential Building (Apartment / Condo)  0.13
2                            Medical Center  0.04
3                          Dentist's Office  0.04
4              General College & University  0.02


----Remsen Village----
                  venue  freq
0    Salon / Barbershop  0.12
1                Church  0.07
2       Automotive Shop  0.05
3  Caribbean Restaurant  0.05
4       Laundry Service  0.03


----Richmond Hill----
               venue  freq
0        Pizza Place  0.06
1  Convenience Store  0.05
2             Bakery  0.05
3         Nail Salon  0.04
4    Doctor's Office  0.04


----Richmond Town----
                venue  freq
0    Dentist's Office  0.07
1  Italian Restaurant  0.04
2              School  0.04
3       Grocery Store  0.04
4  Miscellaneous Shop  0.04


----Richmond Valley----
                  venue  freq
0    Salon / Barbershop  0.05
1                Bakery  0.04
2  Fast Food Restaurant  0.04
3     Convenience Store  0.03
4            Nail Salon  0.03


----Ridgewood----
                                      venue  freq
0                             Deli / Bodega  0.07
1                            Hardware Store  0.05
2  Residential Building (Apartment / Condo)  0.04
3                        Salon / Barbershop  0.04
4                            Cosmetics Shop  0.04


----Riverdale----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.24
1                                 Synagogue  0.09
2                           Doctor's Office  0.06
3                                      Park  0.05
4                                Playground  0.05


----Rochdale----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.14
1                           Doctor's Office  0.07
2                                Nail Salon  0.06
3                           Laundry Service  0.05
4                                      Park  0.04


----Rockaway Beach----
                                      venue  freq
0                             Deli / Bodega  0.08
1                                     Beach  0.08
2  Residential Building (Apartment / Condo)  0.08
3                               Pizza Place  0.03
4                        Seafood Restaurant  0.03


----Rockaway Park----
             venue  freq
0            Beach  0.12
1  Doctor's Office  0.08
2       Eye Doctor  0.08
3        Synagogue  0.04
4            Track  0.04


----Roosevelt Island----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.09
1                                    Church  0.08
2                           Laundry Service  0.05
3                                      Taxi  0.05
4                               Art Gallery  0.04


----Rosebank----
                   venue  freq
0            Pizza Place  0.06
1     Italian Restaurant  0.04
2     Mexican Restaurant  0.04
3         Ice Cream Shop  0.04
4  General Entertainment  0.04


----Rosedale----
                  venue  freq
0    Salon / Barbershop  0.07
1  Caribbean Restaurant  0.06
2           Gas Station  0.04
3        Cosmetics Shop  0.04
4       Laundry Service  0.03


----Rossville----
                   venue  freq
0  General Entertainment  0.06
1            Pizza Place  0.05
2     Athletics & Sports  0.04
3                    Bar  0.04
4         Medical Center  0.04


----Roxbury----
                venue  freq
0               Beach  0.10
1                 Bar  0.05
2      Hardware Store  0.03
3      Baseball Field  0.03
4  Seafood Restaurant  0.03


----Rugby----
                  venue  freq
0  Caribbean Restaurant  0.09
1    Salon / Barbershop  0.08
2       Laundry Service  0.06
3    Chinese Restaurant  0.06
4                  Food  0.05


----Sandy Ground----
                   venue  freq
0  General Entertainment  0.06
1      Elementary School  0.04
2     Athletics & Sports  0.04
3   Other Great Outdoors  0.04
4       Dentist's Office  0.03


----Schuylerville----
                         venue  freq
0              Doctor's Office  0.19
1           Salon / Barbershop  0.08
2                Deli / Bodega  0.07
3  Professional & Other Places  0.05
4                     Pharmacy  0.03


----Sea Gate----
                                      venue  freq
0                                     Beach  0.10
1                       Housing Development  0.06
2  Residential Building (Apartment / Condo)  0.04
3                               Yoga Studio  0.03
4                              Optical Shop  0.03


----Sheepshead Bay----
                                      venue  freq
0                           Harbor / Marina  0.10
1  Residential Building (Apartment / Condo)  0.06
2                             Boat or Ferry  0.05
3                      Other Great Outdoors  0.04
4                                      Park  0.04


----Shore Acres----
                venue  freq
0       Deli / Bodega  0.08
1  Italian Restaurant  0.05
2  Salon / Barbershop  0.05
3  Miscellaneous Shop  0.04
4    Dentist's Office  0.03


----Silver Lake----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.10
1                           College Library  0.07
2              General College & University  0.05
3                                  Cemetery  0.04
4           College Administrative Building  0.04


----Soho----
            venue  freq
0        Boutique  0.11
1  Clothing Store  0.11
2   Jewelry Store  0.06
3   Women's Store  0.06
4     Event Space  0.05


----Somerville----
             venue  freq
0  Automotive Shop  0.06
1  Harbor / Marina  0.04
2   Sandwich Place  0.04
3             Farm  0.03
4   Farmers Market  0.03


----Soundview----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.09
1                       Housing Development  0.06
2                           Laundry Service  0.05
3                               Pizza Place  0.04
4                             Grocery Store  0.04


----South Beach----
              venue  freq
0          Hospital  0.13
1    Medical Center  0.10
2   Doctor's Office  0.09
3             Beach  0.05
4  Dentist's Office  0.03


----South Jamaica----
                venue  freq
0     Automotive Shop  0.13
1              Church  0.12
2  Salon / Barbershop  0.05
3     Laundry Service  0.04
4         Pizza Place  0.04


----South Ozone Park----
                 venue  freq
0          Gas Station  0.09
1  Government Building  0.08
2        Deli / Bodega  0.05
3                  Bar  0.04
4                 Park  0.04


----South Side----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.13
1                        Salon / Barbershop  0.08
2                         Indian Restaurant  0.04
3                                  Tea Room  0.03
4                             Deli / Bodega  0.03


----Springfield Gardens----
                venue  freq
0              Church  0.09
1         Gas Station  0.08
2     Automotive Shop  0.05
3  Chinese Restaurant  0.05
4       Deli / Bodega  0.05


----Spuyten Duyvil----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.18
1                           Doctor's Office  0.10
2                            Medical Center  0.06
3                        Salon / Barbershop  0.04
4                          Dentist's Office  0.04


----St. Albans----
                  venue  freq
0    Salon / Barbershop  0.10
1  Caribbean Restaurant  0.07
2         Deli / Bodega  0.07
3       Laundry Service  0.06
4                Church  0.05


----St. George----
                                      venue  freq
0                                Courthouse  0.07
1                             Deli / Bodega  0.07
2                       Government Building  0.05
3                        Chinese Restaurant  0.04
4  Residential Building (Apartment / Condo)  0.04


----Stapleton----
                venue  freq
0                Food  0.08
1  Salon / Barbershop  0.08
2         Pizza Place  0.07
3              Church  0.07
4       Deli / Bodega  0.05


----Starrett City----
                                      venue  freq
0                           Doctor's Office  0.10
1                           Laundry Service  0.07
2  Residential Building (Apartment / Condo)  0.06
3                                   Parking  0.06
4                                  Pharmacy  0.04


----Steinway----
                  venue  freq
0       Automotive Shop  0.06
1         Deli / Bodega  0.05
2           Pizza Place  0.05
3  Gym / Fitness Center  0.05
4   Rental Car Location  0.04


----Stuyvesant Town----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.42
1                                Playground  0.13
2                                      Park  0.06
3                           Coworking Space  0.04
4                              Tech Startup  0.04


----Sunnyside Gardens----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.08
1                           Laundry Service  0.07
2                                       Spa  0.05
3                             Grocery Store  0.05
4                                Nail Salon  0.05


----Sunnyside, Queens----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.10
1                             Deli / Bodega  0.07
2                               Pizza Place  0.07
3                           Laundry Service  0.06
4                        Salon / Barbershop  0.05


----Sunnyside, Staten Island----
                    venue  freq
0         Doctor's Office  0.10
1                 Theater  0.08
2  College Residence Hall  0.07
3         College Library  0.04
4            Dance Studio  0.04


----Sunset Park----
                venue  freq
0  Salon / Barbershop  0.06
1         Pizza Place  0.06
2        Optical Shop  0.05
3  Mexican Restaurant  0.04
4       Women's Store  0.04


----Sutton Place----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.09
1                                   Parking  0.05
2                      Gym / Fitness Center  0.04
3                                      Taxi  0.04
4                               Yoga Studio  0.02


----Throgs Neck----
                                      venue  freq
0                      Other Great Outdoors  0.09
1                        Chinese Restaurant  0.05
2                        Italian Restaurant  0.05
3                             Deli / Bodega  0.04
4  Residential Building (Apartment / Condo)  0.04


----Todt Hill----
                         venue  freq
0              Doctor's Office  0.15
1  Professional & Other Places  0.04
2           Salon / Barbershop  0.03
3                   Nail Salon  0.03
4             Sushi Restaurant  0.03


----Tompkinsville----
                  venue  freq
0    Salon / Barbershop  0.10
1    Mexican Restaurant  0.08
2                Church  0.05
3  Caribbean Restaurant  0.05
4           Pizza Place  0.05


----Tottenville----
                venue  freq
0  Italian Restaurant  0.08
1              School  0.06
2              Temple  0.05
3       Deli / Bodega  0.03
4               Beach  0.03


----Travis----
                venue  freq
0                Park  0.05
1               Hotel  0.05
2     Automotive Shop  0.05
3  Italian Restaurant  0.04
4         Pizza Place  0.04


----Tribeca----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.07
1                                      Café  0.04
2                                Food Truck  0.04
3                     General Entertainment  0.04
4                       American Restaurant  0.04


----Tudor City----
                                      venue  freq
0                           Doctor's Office  0.17
1  Residential Building (Apartment / Condo)  0.15
2                            Medical Center  0.06
3                                       Gym  0.05
4                           Laundry Service  0.04


----Turtle Bay----
                                      venue  freq
0                       Embassy / Consulate  0.13
1                       Government Building  0.13
2  Residential Building (Apartment / Condo)  0.12
3                                Non-Profit  0.03
4                                    Tunnel  0.03


----Unionport----
                venue  freq
0  Salon / Barbershop  0.12
1     Laundry Service  0.07
2       Deli / Bodega  0.06
3     Other Nightlife  0.03
4              Church  0.03


----University Heights----
                       venue  freq
0  College Academic Building  0.08
1              Deli / Bodega  0.07
2         Salon / Barbershop  0.05
3                        Bar  0.03
4                Supermarket  0.03


----Upper East Side----
                                      venue  freq
0                           Doctor's Office  0.31
1                               Art Gallery  0.10
2  Residential Building (Apartment / Condo)  0.10
3                          Dentist's Office  0.06
4                            Medical Center  0.06


----Upper West Side----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.11
1                                Shoe Store  0.04
2                        Salon / Barbershop  0.04
3                           Laundry Service  0.04
4                                Nail Salon  0.04


----Utopia----
               venue  freq
0    Automotive Shop  0.07
1      Deli / Bodega  0.06
2    Laundry Service  0.04
3         Playground  0.04
4  Elementary School  0.04


----Van Nest----
                venue  freq
0       Deli / Bodega  0.07
1  Salon / Barbershop  0.05
2          Nail Salon  0.05
3     Laundry Service  0.05
4         Pizza Place  0.04


----Vinegar Hill----
                                      venue  freq
0                              Tech Startup  0.11
1                               Art Gallery  0.07
2  Residential Building (Apartment / Condo)  0.05
3                               Event Space  0.04
4                     General Entertainment  0.04


----Wakefield----
                venue  freq
0  Salon / Barbershop  0.07
1     Laundry Service  0.06
2                Food  0.03
3     Doctor's Office  0.03
4              Church  0.03


----Washington Heights----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.17
1                           Doctor's Office  0.10
2                        Salon / Barbershop  0.05
3                             Metro Station  0.05
4                                   Daycare  0.05


----Weeksville----
                                      venue  freq
0                           Automotive Shop  0.06
1                      Caribbean Restaurant  0.06
2                        Salon / Barbershop  0.06
3                        Chinese Restaurant  0.04
4  Residential Building (Apartment / Condo)  0.04


----West Brighton----
                venue  freq
0     Doctor's Office  0.06
1                Bank  0.05
2           Gift Shop  0.03
3          Bagel Shop  0.03
4  Salon / Barbershop  0.03


----West Farms----
                                      venue  freq
0                           Automotive Shop  0.06
1                           Doctor's Office  0.04
2                                    School  0.04
3  Residential Building (Apartment / Condo)  0.04
4                                Donut Shop  0.03


----West Village----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.11
1                              Cocktail Bar  0.06
2                        Salon / Barbershop  0.04
3                           Laundry Service  0.04
4                              Antique Shop  0.03


----Westchester Square----
                  venue  freq
0  Fast Food Restaurant  0.03
1       Automotive Shop  0.03
2            Nail Salon  0.03
3        Cosmetics Shop  0.03
4     Convenience Store  0.03


----Westerleigh----
                        venue  freq
0             Doctor's Office  0.06
1          Salon / Barbershop  0.04
2            Sushi Restaurant  0.03
3                        Bank  0.03
4  Financial or Legal Service  0.03


----Whitestone----
                venue  freq
0       Deli / Bodega  0.10
1  Italian Restaurant  0.06
2  Salon / Barbershop  0.04
3                 Gym  0.04
4   Convenience Store  0.03


----Williamsbridge----
                  venue  freq
0                Church  0.09
1    Salon / Barbershop  0.08
2  Caribbean Restaurant  0.06
3                School  0.04
4                   Spa  0.03


----Williamsburg----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.08
1                               Pizza Place  0.06
2                        Salon / Barbershop  0.05
3                        Chinese Restaurant  0.04
4                                      Park  0.04


----Willowbrook----
             venue  freq
0        Synagogue  0.07
1    Deli / Bodega  0.06
2           Church  0.06
3  Laundry Service  0.04
4     Dance Studio  0.04


----Windsor Terrace----
             venue  freq
0    Middle School  0.04
1           Bridge  0.04
2    Deli / Bodega  0.04
3   Hardware Store  0.04
4  Thai Restaurant  0.04


----Wingate----
                venue  freq
0  Salon / Barbershop  0.07
1              School  0.05
2                Food  0.04
3       Deli / Bodega  0.04
4         Event Space  0.04


----Woodhaven----
                venue  freq
0       Deli / Bodega  0.11
1  Salon / Barbershop  0.09
2     Laundry Service  0.06
3  Miscellaneous Shop  0.05
4        Liquor Store  0.04


----Woodlawn----
                venue  freq
0                 Bar  0.11
1  Salon / Barbershop  0.06
2       Deli / Bodega  0.06
3         Pizza Place  0.04
4              Church  0.04


----Woodrow----
               venue  freq
0               Pool  0.05
1             School  0.04
2      Grocery Store  0.04
3  Fish & Chips Shop  0.02
4          Gift Shop  0.02


----Woodside----
                venue  freq
0  Salon / Barbershop  0.07
1                 Bar  0.07
2  Mexican Restaurant  0.05
3            Platform  0.04
4     Thai Restaurant  0.04


----Yorkville----
                                      venue  freq
0  Residential Building (Apartment / Condo)  0.25
1                           Laundry Service  0.06
2                                       Spa  0.05
3                                       Gym  0.03
4                        Salon / Barbershop  0.03

The most common categories for each neighborhood¶

First, let's write a function to sort the venues in descending order.

def return_most_common_venues(row, num_top_cat):
    row_categories = row.iloc[1:]
    row_categories_sorted = row_categories.sort_values(ascending=False)
    
    return row_categories_sorted.index.values[0:num_top_cat]

num_top_cat = 10

indicators  = ['st', 'nd', 'rd']

# create columns according to number of top venues
columns = ['Neighborhood']
for ind in np.arange(num_top_cat):
    try:
        columns.append('{}{} Most Common Category'.format(ind+1, indicators[ind]))
    except:
        columns.append('{}th Most Common Category'.format(ind+1))

# create a new dataframe
nyc_neighborhoods_categories_sorted = pd.DataFrame(columns=columns)
nyc_neighborhoods_categories_sorted['Neighborhood'] = nyc_grouped['Neighborhood']

for ind in np.arange(nyc_grouped.shape[0]):
    nyc_neighborhoods_categories_sorted.iloc[ind, 1:] = return_most_common_venues(
        nyc_grouped.iloc[ind, :], num_top_cat)

nyc_neighborhoods_categories_sorted.head()

1.3 Clustering¶

Now we apply K-means clustering on the dataframe stored in nyc_grouped variable which includes the relative frequency of each venue-category for each neighborhood.

# set number of clusters
kclusters = 5

nyc_grouped_clustering = nyc_grouped.drop('Neighborhood', 1)

# run k-means clustering
kmeans = KMeans(n_clusters=kclusters, random_state=0).fit(nyc_grouped_clustering)

# check cluster labels generated for each row in the dataframe
kmeans.labels_[0:10]

array([3, 1, 1, 1, 1, 1, 0, 1, 1, 1], dtype=int32)

# add clustering labels
nyc_neighborhoods_categories_sorted.insert(0, 'Cluster Labels', kmeans.labels_)

nyc_merged = nyc_neighborhoods.rename(columns={'Neighborhood': 'Neighborhood'}).copy()
nyc_merged = nyc_merged[~nyc_merged['Neighborhood'].isin(nyc_excluded_neighborhoods)]

# merge toronto_grouped with toronto_data to add latitude/longitude for each neighborhood
nyc_merged = nyc_merged.join(nyc_neighborhoods_categories_sorted.set_index('Neighborhood'), on='Neighborhood')

nyc_merged.head() # check the last columns!

nyc_merged['Cluster Labels']

0      3
1      4
2      1
3      1
4      0
5      1
6      3
7      3
8      2
9      3
10     1
11     2
12     1
13     0
14     3
15     4
16     1
17     3
18     1
19     4
20     4
21     0
22     4
23     1
24     1
25     4
26     4
27     1
28     1
29     1
30     4
31     1
32     3
33     2
34     3
35     0
36     1
37     1
38     2
39     1
40     1
41     3
42     2
43     4
44     3
45     4
46     4
47     1
48     1
49     1
50     1
51     1
52     1
53     2
54     0
55     4
56     3
57     1
58     1
59     4
60     4
61     4
62     4
63     1
64     2
65     4
66     4
67     1
68     1
69     4
70     4
71     3
72     3
73     2
74     3
75     3
76     1
77     1
78     4
79     1
80     1
81     2
82     1
83     1
84     4
85     1
86     1
87     3
88     4
89     4
90     3
91     1
92     4
93     0
94     2
95     4
96     4
97     4
98     4
99     2
100    1
101    0
102    3
103    4
104    4
105    4
106    4
107    2
108    0
109    4
110    4
111    4
112    1
113    1
114    1
115    2
116    1
117    4
118    4
119    0
120    4
121    1
122    1
123    4
124    3
125    1
126    2
127    4
128    1
129    0
130    3
131    0
132    4
133    1
134    3
135    2
136    4
137    1
138    1
139    4
140    4
141    3
142    1
143    1
144    2
145    2
146    3
147    1
148    1
149    1
150    1
151    1
152    1
153    1
154    1
155    1
156    1
157    1
158    4
159    0
160    1
161    1
162    1
163    3
164    3
165    3
166    0
167    1
168    3
169    3
170    3
171    1
172    1
173    1
174    1
175    0
176    1
177    1
178    4
179    1
180    1
181    1
182    2
183    1
184    1
185    1
186    4
187    2
188    3
189    0
190    1
191    1
192    1
193    1
194    3
195    3
196    0
197    1
198    3
199    3
200    1
201    1
202    4
203    2
204    2
205    1
206    1
207    1
208    2
209    2
210    1
211    1
212    2
213    3
214    1
215    1
216    1
217    1
218    3
219    4
220    2
221    0
222    3
223    3
224    4
225    1
226    1
227    1
228    1
229    1
230    1
231    2
232    1
233    1
234    1
235    1
236    1
237    3
238    1
239    1
240    1
241    1
242    2
243    2
244    1
245    2
246    2
247    4
248    4
249    1
250    1
251    1
252    3
253    1
254    2
255    2
256    2
257    1
258    3
259    3
260    3
261    1
262    1
263    3
264    1
265    2
266    1
267    3
268    4
269    1
270    3
271    4
272    1
273    4
274    2
275    0
276    1
277    4
278    1
279    1
280    1
281    3
282    1
283    1
284    1
285    1
286    1
287    2
288    1
289    3
290    1
291    1
292    1
293    1
294    1
295    3
296    2
297    4
298    3
299    4
300    3
301    1
302    4
303    1
304    1
305    4
Name: Cluster Labels, dtype: int32

Creating a map that shows the neighborhoods and their clusters¶

We will create a map that shows a marker for each neighborhood; the color of the marker represents the cluster of the neighborhood.

# create map
map_clusters = folium.Map(location  = [latitude, longitude], 
                          zoom_start= 10,
                          min_zoom  = 9, 
                          max_zoom  = 11)

# # set color scheme for the clusters
# colors = {
#     nyc_borough_list[0]:'red',
#     nyc_borough_list[1]:'blue',
#     nyc_borough_list[2]:'yellow',
#     nyc_borough_list[3]:'green',
#     nyc_borough_list[4]:'orange'}

# add markers to the map
for lat, lon, poi, cluster in zip(nyc_merged['Latitude'], nyc_merged['Longitude'], 
                                  nyc_merged['Neighborhood'], nyc_merged['Cluster Labels']):
    label = folium.Popup(str(poi) + ' Cluster ' + str(cluster), parse_html=True)
    folium.CircleMarker(
        [lat, lon],
        radius=3,
        weight=1,
        popup =label,
        color ='black',
        fill  =True,
        fill_color=colors[nyc_borough_list[cluster]],
        fill_opacity=0.8).add_to(map_clusters)
       
map_clusters

Examining clusters¶

Let's see the neighborhoods in each of the five clusters:

nyc_merged.loc[nyc_merged['Cluster Labels'] == 0, 
               nyc_merged.columns[[1] + list(range(5, nyc_merged.shape[1]))]]

nyc_merged.loc[nyc_merged['Cluster Labels'] == 1, 
               nyc_merged.columns[[1] + list(range(5, nyc_merged.shape[1]))]]

nyc_merged.loc[nyc_merged['Cluster Labels'] == 2, 
               nyc_merged.columns[[1] + list(range(5, nyc_merged.shape[1]))]]

nyc_merged.loc[nyc_merged['Cluster Labels'] == 3, 
               nyc_merged.columns[[1] + list(range(5, nyc_merged.shape[1]))]]

nyc_merged.loc[nyc_merged['Cluster Labels'] == 4, 
               nyc_merged.columns[[1] + list(range(5, nyc_merged.shape[1]))]]

2. Data for Toronto¶

2.1 Coordinates of neighborhoods in Toronto¶

Downloading and saving neighborhoods Data for Toronto¶

# !conda install -c anaconda lxml python=3.6 -y

# # # import pandas as pd
# tor_neighborhoodss = pd.read_html('https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M')
# tor_neighborhoods  = tor_neighborhoodss[0] 
# # tor_neighborhoods.head()
# # tor_neighborhoods.to_pickle("./capstone_data/tor_nbhds.pkl")
# # tor_neighborhoods.head()
# # tor_neighborhoods.columns
# # # 

# tor_neighborhoods.to_csv('./capstone_data/tor_nbhds.csv',index=False)

tor_neighborhoods = pd.read_csv('./capstone_data/tor_nbhds.csv')
# tor_neighborhoods = pd.read_pickle("./capstone_data/tor_nbhds.pkl")
tor_neighborhoods.columns

Index(['Postal Code', 'Borough', 'Neighbourhood'], dtype='object')

print('The dataframe has {} boroughs and {} neighborhoods.'.format(
        len(tor_neighborhoods['Borough'].unique()),
        tor_neighborhoods.shape[0]
    )
)

The dataframe has 11 boroughs and 180 neighborhoods.

tor_neighborhoods.columns = ['PostalCode', 'Borough', 'Neighborhood']

tor_neighborhoods.head()

Removing records where the borough is "not assigned"¶

indexNames = tor_neighborhoods[tor_neighborhoods['Borough'] == 'Not assigned'].index
indexNames

Int64Index([  0,   1,   7,  10,  15,  16,  19,  24,  25,  28,  29,  33,  34,
             35,  37,  38,  42,  43,  44,  51,  52,  53,  60,  61,  62,  69,
             70,  71,  78,  79,  87,  88,  96,  97, 101, 105, 106, 110, 115,
            118, 119, 123, 124, 125, 127, 128, 131, 132, 133, 134, 136, 137,
            140, 141, 145, 146, 149, 150, 154, 155, 158, 159, 161, 162, 163,
            164, 166, 167, 170, 171, 172, 173, 174, 175, 176, 177, 179],
           dtype='int64')

tor_neighborhoods.drop(indexNames, inplace = True)

For example, there are two neighborhoods (Harbourfront and Regent Park) that share the same postal code and the same borough (Downtown Toronto) as shown below:

tor_neighborhoods[tor_neighborhoods.PostalCode == 'M5A']

tor_neighborhoods = (tor_neighborhoods.groupby(['PostalCode', 'Borough'])['Neighborhood']
      .apply(lambda x: "{}".format(', '.join(x))).reset_index())

Now let's take a look at the record of Downtown Toronto borough:

tor_neighborhoods[tor_neighborhoods.PostalCode == 'M5A']

Getting Latitude and Longitude Coordinates of the Neighborhoods¶

# !wget -O ./capstone_data/toronto_nbhds_coords.csv http://cocl.us/Geospatial_data

tor_lat_lng_df = pd.read_csv('./capstone_data/toronto_nbhds_coords.csv')
tor_lat_lng_df.head()

Adding latitude and longitude data to the neighborhood dataframe¶

tor_neighborhoods.reset_index(drop=True, inplace=True)
tor_neighborhoods['Latitude'] = -99999.9
tor_neighborhoods['Longitude'] = -99999.9

for i in range(tor_neighborhoods.shape[0]):
    postalcode = tor_neighborhoods.loc[i, 'PostalCode']
    lat = tor_lat_lng_df.loc[tor_lat_lng_df['Postal Code'] == postalcode, 'Latitude'].squeeze()
    lng = tor_lat_lng_df.loc[tor_lat_lng_df['Postal Code'] == postalcode, 'Longitude'].squeeze()
    tor_neighborhoods.loc[i, 'Latitude'] = lat
    tor_neighborhoods.loc[i, 'Longitude'] = lng

tor_neighborhoods.head()

tor_neighborhoods.shape

(103, 5)

Using `geopy` Library to get the coordinates of Toronto¶

address = 'Toronto'
geolocator = Nominatim(user_agent="ny_explorer")
location = geolocator.geocode(address)
latitude = location.latitude
longitude = location.longitude
print('The geograpical coordinate of Toronto are {}, {}.'.format(latitude, longitude))

The geograpical coordinate of Toronto are 43.6534817, -79.3839347.

Creating a map of Toronto with neighborhoods superimposed on top¶

map_toronto = folium.Map(location=[latitude, longitude], zoom_start=10,
                         min_zoom=9, max_zoom=11)

# add neighborhood markers to map
for lat, lng, borough, neighborhood in zip(tor_neighborhoods['Latitude'], tor_neighborhoods['Longitude'], 
                                           tor_neighborhoods['Borough'], tor_neighborhoods['Neighborhood']):
    label = '{}, {}'.format(neighborhood, borough)
    label = folium.Popup(label, parse_html=True)
    folium.CircleMarker(
        [lat, lng],
        radius=3,
        weight=2,
        popup=label,
        color='#333333',
        fill=True,
        fill_color='#388e3c',
        fill_opacity=0.7,
        parse_html=False).add_to(map_toronto)  
    
map_toronto

2.2 Toronto Venue Data (Latitude, Longitude, Category)¶

Downloading and saving Venue Data For Toronto from Foursquare¶

# We only need to do this once.
# df_tor_nbhd = getNearbyVenues(names     = tor_neighborhoods['Neighborhood'],
#                              latitudes  = tor_neighborhoods['Latitude'],
#                              longitudes = tor_neighborhoods['Longitude']
#                             )

# df_tor_nbhd.to_csv('./capstone_data/tor_venues.csv',index=False)

Loading the saved data into a pandas dataframe¶

tor_venues = pd.read_csv("./capstone_data/tor_venues.csv")

# Removing records where venue is "building" or "office"
tor_venues = tor_venues[~tor_venues['Venue Category'].isin([
    'Building', 'Office', 'Bus Line', 'Bus Station', 'Bus Stop', 'Road'])]
print(tor_venues.shape)
tor_venues.head()

(7858, 7)

Let's check how many venues were returned for each neighborhood¶

tor_venues.groupby('Neighborhood').size()

Neighborhood
Agincourt                                                                                                                                  80
Alderwood, Long Branch                                                                                                                     81
Bathurst Manor, Wilson Heights, Downsview North                                                                                            82
Bayview Village                                                                                                                            79
Bedford Park, Lawrence Manor East                                                                                                          87
Berczy Park                                                                                                                                65
Birch Cliff, Cliffside West                                                                                                                74
Brockton, Parkdale Village, Exhibition Place                                                                                               65
Business reply mail Processing Centre, South Central Letter Processing Plant Toronto                                                       71
CN Tower, King and Spadina, Railway Lands, Harbourfront West, Bathurst Quay, South Niagara, Island airport                                 98
Caledonia-Fairbanks                                                                                                                        83
Canada Post Gateway Processing Centre                                                                                                      56
Cedarbrae                                                                                                                                  78
Central Bay Street                                                                                                                         90
Christie                                                                                                                                   78
Church and Wellesley                                                                                                                       79
Clarks Corners, Tam O'Shanter, Sullivan                                                                                                    86
Cliffside, Cliffcrest, Scarborough Village West                                                                                            88
Commerce Court, Victoria Hotel                                                                                                             73
Davisville                                                                                                                                 80
Davisville North                                                                                                                           65
Del Ray, Mount Dennis, Keelsdale and Silverthorn                                                                                           75
Don Mills                                                                                                                                 137
Dorset Park, Wexford Heights, Scarborough Town Centre                                                                                      89
Downsview                                                                                                                                 311
Dufferin, Dovercourt Village                                                                                                               86
East Toronto, Broadview North (Old East York)                                                                                              71
Eringate, Bloordale Gardens, Old Burnhamthorpe, Markland Wood                                                                              80
Fairview, Henry Farm, Oriole                                                                                                               94
First Canadian Place, Underground city                                                                                                     89
Forest Hill North & West, Forest Hill Road Park                                                                                            68
Garden District, Ryerson                                                                                                                   90
Glencairn                                                                                                                                  80
Golden Mile, Clairlea, Oakridge                                                                                                            72
Guildwood, Morningside, West Hill                                                                                                          60
Harbourfront East, Union Station, Toronto Islands                                                                                          57
High Park, The Junction South                                                                                                              38
Hillcrest Village                                                                                                                          88
Humber Summit                                                                                                                              77
Humberlea, Emery                                                                                                                           68
Humewood-Cedarvale                                                                                                                         64
India Bazaar, The Beaches West                                                                                                             82
Islington Avenue, Humber Valley Village                                                                                                    51
Kennedy Park, Ionview, East Birchmount Park                                                                                                82
Kensington Market, Chinatown, Grange Park                                                                                                  92
Kingsview Village, St. Phillips, Martin Grove Gardens, Richview Gardens                                                                    67
Lawrence Manor, Lawrence Heights                                                                                                           86
Lawrence Park                                                                                                                              71
Leaside                                                                                                                                    90
Little Portugal, Trinity                                                                                                                   88
Malvern, Rouge                                                                                                                             81
Milliken, Agincourt North, Steeles East, L'Amoreaux East                                                                                   59
Mimico NW, The Queensway West, South of Bloor, Kingsway Park South West, Royal York South West                                             84
Moore Park, Summerhill East                                                                                                                71
New Toronto, Mimico South, Humber Bay Shores                                                                                               75
North Park, Maple Leaf Park, Upwood Park                                                                                                   75
North Toronto West, Lawrence Park                                                                                                          81
Northwest, West Humber - Clairville                                                                                                        70
Northwood Park, York University                                                                                                            76
Old Mill South, King's Mill Park, Sunnylea, Humber Bay, Mimico NE, The Queensway East, Royal York South East, Kingsway Park South East     50
Parkdale, Roncesvalles                                                                                                                     78
Parkview Hill, Woodbine Gardens                                                                                                            71
Parkwoods                                                                                                                                  75
Queen's Park, Ontario Provincial Government                                                                                                66
Regent Park, Harbourfront                                                                                                                  77
Richmond, Adelaide, King                                                                                                                   83
Rosedale                                                                                                                                   69
Roselawn                                                                                                                                   61
Rouge Hill, Port Union, Highland Creek                                                                                                     78
Runnymede, Swansea                                                                                                                         89
Runnymede, The Junction North                                                                                                              90
Scarborough Village                                                                                                                        82
South Steeles, Silverstone, Humbergate, Jamestown, Mount Olive, Beaumond Heights, Thistletown, Albion Gardens                              94
St. James Town                                                                                                                             72
St. James Town, Cabbagetown                                                                                                                86
Steeles West, L'Amoreaux West                                                                                                              93
Stn A PO Boxes                                                                                                                             39
Studio District                                                                                                                            78
Summerhill West, Rathnelly, South Hill, Forest Hill SE, Deer Park                                                                          68
The Annex, North Midtown, Yorkville                                                                                                        73
The Beaches                                                                                                                                81
The Danforth West, Riverdale                                                                                                               91
The Kingsway, Montgomery Road, Old Mill North                                                                                              81
Thorncliffe Park                                                                                                                           85
Toronto Dominion Centre, Design Exchange                                                                                                   74
University of Toronto, Harbord                                                                                                             91
Upper Rouge                                                                                                                                74
Victoria Village                                                                                                                           77
West Deane Park, Princess Gardens, Martin Grove, Islington, Cloverdale                                                                     68
Westmount                                                                                                                                  86
Weston                                                                                                                                     85
Wexford, Maryvale                                                                                                                          85
Willowdale, Newtonbrook                                                                                                                    82
Willowdale, Willowdale East                                                                                                                90
Willowdale, Willowdale West                                                                                                                65
Woburn                                                                                                                                     78
Woodbine Heights                                                                                                                           62
York Mills West                                                                                                                            76
York Mills, Silver Hills                                                                                                                   42
dtype: int64

tor_neighborhoods['Neighborhood'].unique().shape[0], tor_venues['Neighborhood'].unique().shape[0]

(99, 99)

# In case Foursquare does not return any venue for some neighborhoods.
tor_excluded_neighborhoods = set(tor_neighborhoods['Neighborhood']).difference(set(tor_venues['Neighborhood']))
tor_excluded_neighborhoods

set()

Let's find out how many unique categories can be curated from all the returned venues¶

print('There are {} uniques categories.'.format(len(tor_venues['Venue Category'].unique())))

There are 508 uniques categories.

Performing one-hot encoding on the `Venue Category` variable¶

'Neighborhood' in tor_venues['Venue Category'].unique()

False

# one hot encoding
tor_onehot = pd.get_dummies(tor_venues[['Venue Category']], prefix="", prefix_sep="")

# # add neighborhood column back to dataframe
# # we used Neighborhood_" instead of just "Neighborhood" because
# # there is a venue category called "Neighborhood"
# tor_onehot['Neighborhood_'] = tor_venues['Neighborhood'] 
tor_onehot['Neighborhood'] = tor_venues['Neighborhood'] 

# move neighborhood column to the first column
fixed_columns = [tor_onehot.columns[-1]] + list(tor_onehot.columns[:-1])
tor_onehot = tor_onehot[fixed_columns]

tor_onehot.head()

Grouping rows by neighborhood and taking the mean of the frequency of occurrence of each category for each neighborhood¶

# tor_grouped = tor_onehot.groupby('Neighborhood_').mean().reset_index()
tor_grouped = tor_onehot.groupby('Neighborhood').mean().reset_index()
tor_grouped.head()

The most common categories for each neighborhood¶

def return_most_common_venues(row, num_top_cat):
    row_categories = row.iloc[1:]
    row_categories_sorted = row_categories.sort_values(ascending=False)
    
    return row_categories_sorted.index.values[0:num_top_cat]


num_top_cat = 7
indicators = ['st', 'nd', 'rd']

# create columns according to number of top venues
# columns = ['Neighborhood_']
columns = ['Neighborhood']
for ind in np.arange(num_top_cat):
    try:
        columns.append('{}{} Most Common Category'.format(ind+1, indicators[ind]))
    except:
        columns.append('{}th Most Common Category'.format(ind+1))

# create a new dataframe
tor_neighborhoods_categories_sorted = pd.DataFrame(columns=columns)
# tor_neighborhoods_categories_sorted['Neighborhood_'] = tor_grouped['Neighborhood_']
tor_neighborhoods_categories_sorted['Neighborhood'] = tor_grouped['Neighborhood']

for ind in np.arange(tor_grouped.shape[0]):
    tor_neighborhoods_categories_sorted.iloc[ind, 1:] = return_most_common_venues(
        tor_grouped.iloc[ind, :], num_top_cat)

tor_neighborhoods_categories_sorted.head()

2.3 Clustering¶

Now we apply K-means clustering on the dataframe stored in toronto_grouped variable which includes the relative frequency of each venue-category for each neighborhood.

# set number of clusters
kclusters = 5

# tor_grouped_clustering = tor_grouped.drop('Neighborhood_', 1)
tor_grouped_clustering = tor_grouped.drop('Neighborhood', 1)

# run k-means clustering
kmeans = KMeans(n_clusters=kclusters, random_state=0).fit(tor_grouped_clustering)

# check cluster labels generated for each row in the dataframe
kmeans.labels_[0:10]

array([1, 2, 0, 0, 2, 2, 0, 4, 2, 3], dtype=int32)

# add clustering labels
tor_neighborhoods_categories_sorted.insert(0, 'Cluster Labels', kmeans.labels_)

# tor_merged = tor_neighborhoods.rename(columns={'Neighborhood': 'Neighborhood_'}).copy()
# tor_merged = tor_merged[~tor_merged['Neighborhood_'].isin(tor_excluded_neighborhoods)]
tor_merged = tor_neighborhoods.rename(columns={'Neighborhood': 'Neighborhood'}).copy()
tor_merged = tor_merged[~tor_merged['Neighborhood'].isin(tor_excluded_neighborhoods)]

# # merge toronto_grouped with toronto_data to add latitude/longitude for each neighborhood
# tor_merged = tor_merged.join(tor_neighborhoods_categories_sorted.set_index('Neighborhood_'), on='Neighborhood_')
tor_merged = tor_merged.join(tor_neighborhoods_categories_sorted.set_index('Neighborhood'), on='Neighborhood')

tor_merged.head() # check the last columns!

Creating a map that shows the neighborhoods and their clusters¶

We will create a map that shows a marker for each neighborhood; the color of the marker represents the cluster of the neighborhood.

# create map
map_clusters = folium.Map(location=[latitude, longitude], zoom_start=9,
                          min_zoom=8, max_zoom=10)

# set color scheme for the clusters
rainbow = pc[:5]

# add markers to the map
# for lat, lon, poi, cluster in zip(tor_merged['Latitude'], tor_merged['Longitude'], 
#                                   tor_merged['Neighborhood_'], tor_merged['Cluster Labels']):
for lat, lon, poi, cluster in zip(tor_merged['Latitude'], tor_merged['Longitude'], 
                                  tor_merged['Neighborhood'], tor_merged['Cluster Labels']):
    label = folium.Popup(str(poi) + ' Cluster ' + str(cluster), parse_html=True)
    folium.CircleMarker(
        [lat, lon],
        radius=3,
        weight=1,
        popup=label,
        color='#333333',
        fill=True,
        fill_color=rainbow[cluster-1],
        fill_opacity=0.9).add_to(map_clusters)
       
map_clusters

Examining clusters¶

Let's see the neighborhoods in each of the five clusters:

tor_merged.loc[tor_merged['Cluster Labels'] == 0, 
               tor_merged.columns[[1] + list(range(5, tor_merged.shape[1]))]]

tor_merged.loc[tor_merged['Cluster Labels'] == 1, 
               tor_merged.columns[[1] + list(range(5, tor_merged.shape[1]))]]

tor_merged.loc[tor_merged['Cluster Labels'] == 2, 
               tor_merged.columns[[1] + list(range(5, tor_merged.shape[1]))]]

tor_merged.loc[tor_merged['Cluster Labels'] == 3, 
               tor_merged.columns[[1] + list(range(5, tor_merged.shape[1]))]]

tor_merged.loc[tor_merged['Cluster Labels'] == 4, 
               tor_merged.columns[[1] + list(range(5, tor_merged.shape[1]))]]

3. Exploratory Data Analysis¶

Top 10 Common Venue Categories in NYC

Top 10 Common Venue Categories in Toronto
Top 10 Venue Categories in Most NYC Neighborhoods
Top 10 Venue Categories in Most Toronto Neighborhoods
Rare Venue Categories in NYC
Rare Venue Categories in Toronto
Top 10 Common Venue Categories in both NYC and Toronto
Rare Categories in both NYC and Toronto

Top 10 Common Venue Categories in NYC¶

fig, ax = plt.subplots(figsize=fig_size, facecolor=fig_fc)

nyc_top_vc = nyc_venues['Venue Category'].value_counts(normalize=False)

ax = nyc_top_vc.head(10).plot(kind='barh', color=pc[0]);
ax.invert_yaxis()
plot_conf(ax, xlbl='Number of venues', ylbl='Venue category', t='')
plt.tight_layout()
fig.savefig('most-common-ven-nyc.png', dpi=300)

Top 10 Common Venue Categories in Toronto¶

fig, ax = plt.subplots(figsize=fig_size, facecolor=fig_fc)

tor_top_vc = tor_venues['Venue Category'].value_counts(normalize=False)

ax = tor_top_vc.head(10).plot(kind='barh', color=pc[3]);
ax.invert_yaxis()
plot_conf(ax, xlbl='Number of venues', ylbl='Venue category', t='')
plt.tight_layout()
fig.savefig('most-common-ven-tor.png', dpi=300)

Top 10 Venue Categories that Exist in Most NYC Neighborhoods¶

fig, ax = plt.subplots(figsize=fig_size, facecolor=fig_fc)

nyc_g = nyc_onehot.groupby('Neighborhood').max().reset_index()
nyc_p = nyc_g.drop('Neighborhood', axis=1).sum().sort_values(ascending=False)

print("{} neighborhoods in NYC".format(nyc_g.shape[0]))

ax = nyc_p.head(10).plot(kind='barh', color=pc[8]);
ax.invert_yaxis()
plot_conf(ax, xlbl='Number of neighborhoods', ylbl='Venue category', t='')
plt.tight_layout()
fig.savefig('most-common2-ven-nyc.png', dpi=300)

306 neighborhoods in NYC

Top 10 Venue Categories that Exist in Most Toronto Neighborhoods¶

fig, ax = plt.subplots(figsize=fig_size, facecolor=fig_fc)

tor_g = tor_onehot.groupby('Neighborhood').max().reset_index()
tor_p = tor_g.drop('Neighborhood', axis=1).sum().sort_values(ascending=False)

print("{} neighborhoods in Toronto".format(tor_g.shape[0]))
ax = tor_p.head(10).plot(kind='barh', color=pc[14]);
ax.invert_yaxis()
plot_conf(ax, xlbl='Number of neighborhoods', ylbl='Venue category', t='')
plt.tight_layout()
fig.savefig('most-common2-ven-tor.png', dpi=300)

99 neighborhoods in Toronto

Rare Venue Categories in NYC¶

nyc_bot_vc = nyc_venues['Venue Category'].value_counts(normalize=False)
nyc_bot_vc = nyc_bot_vc.tail(10).to_frame('Count')
nyc_bot_vc.index.names = ['Venue Category']
nyc_bot_vc

Rare Venue Categories in Toronto¶

tor_bot_vc = tor_venues['Venue Category'].value_counts(normalize=False)
tor_bot_vc = tor_bot_vc.tail(10).to_frame('Count')
tor_bot_vc.index.names = ['Venue Category']
tor_bot_vc

Combining NYC and Toronto data¶

clmns_nyc_only = set(nyc_grouped.columns).difference(set(tor_grouped.columns))
clmns_tor_only = set(tor_grouped.columns).difference(set(nyc_grouped.columns))

nyc_grouped_ = nyc_grouped.copy()
nyc_grouped_['Neighborhood'] = nyc_grouped_['Neighborhood'].apply(lambda x: x + '_NYC')

tor_grouped_ = tor_grouped.copy()
tor_grouped_['Neighborhood'] = tor_grouped_['Neighborhood'].apply(lambda x: x + '_Toronto')

for c in clmns_tor_only:
    nyc_grouped_[c] = 0
for c in clmns_nyc_only:
    tor_grouped_[c] = 0
    
all_clmns_sorted = ['Neighborhood'] + sorted(list(nyc_grouped_.drop('Neighborhood', axis=1).columns), 
                                              key=str.lower)
nyc_grouped_ = nyc_grouped_[all_clmns_sorted]
tor_grouped_ = tor_grouped_[all_clmns_sorted]
    
nyc_tor_grouped = pd.concat([nyc_grouped_, tor_grouped_]).reset_index(drop=True)

nyc_tor_grouped.loc[303:308,]

nyc_tor_grouped.shape

(405, 630)

# obtaining the most popular categories for each neighborhood
nyc_tor_neighborhoods_categories_sorted = pd.DataFrame(columns=columns)
nyc_tor_neighborhoods_categories_sorted['Neighborhood'] = nyc_tor_grouped['Neighborhood']

for ind in np.arange(nyc_tor_grouped.shape[0]):
    nyc_tor_neighborhoods_categories_sorted.iloc[ind, 1:] = return_most_common_venues(
        nyc_tor_grouped.iloc[ind, :], num_top_cat)

nyc_tor_neighborhoods_categories_sorted.head()

Top 10 Common Venue Categories in both NYC and Toronto¶

fig, ax = plt.subplots(figsize=fig_size, facecolor=fig_fc)
nyc_tor_top_vc = pd.concat([nyc_venues, tor_venues])['Venue Category'].value_counts(normalize=True) * 100
ax = nyc_tor_top_vc.head(10).plot(kind='barh', color=pc[12]);
ax.invert_yaxis()
plot_conf(ax, xlbl='Number of venues', ylbl='Venue category', t='')
fig.savefig('most-common-ven-nyc-tor.png', dpi=300)

Rare Categories in both NYC and Toronto¶

nyc_tor_bot_vc = pd.concat([nyc_venues, tor_venues])['Venue Category'].value_counts(normalize=False)
nyc_tor_bot_vc = nyc_tor_bot_vc.tail(10).to_frame('Count')
nyc_tor_bot_vc.index.names = ['Venue Category']
nyc_tor_bot_vc

Clustering¶

# the number of clusters
kclusters = 5

# nyc_tor_grouped_clustering = nyc_tor_grouped.drop('Neighborhood_', 1)
nyc_tor_grouped_clustering = nyc_tor_grouped.drop('Neighborhood', 1)

# run k-means clustering
kmeans = KMeans(n_clusters=kclusters, random_state=0).fit(nyc_tor_grouped_clustering)

# check cluster labels generated for each row in the dataframe
kmeans.labels_[0:10]

array([3, 3, 4, 4, 3, 4, 2, 3, 0, 3], dtype=int32)

# add clustering labels
nyc_tor_neighborhoods_categories_sorted.insert(0, 'Cluster Labels', kmeans.labels_)

# nyc_tor_merged = nyc_tor_neighborhoods.rename(columns={'Neighborhood': 'Neighborhood_'}).copy()
# tor_merged = tor_merged[~tor_merged['Neighborhood_'].isin(tor_excluded_neighborhoods)]

# # merge toronto_grouped with toronto_data to add latitude/longitude for each neighborhood
# nyc_tor_merged = nyc_tor_neighborhoods_categories_sorted.set_index('Neighborhood_')

# merge toronto_grouped with toronto_data to add latitude/longitude for each neighborhood
nyc_tor_merged = nyc_tor_neighborhoods_categories_sorted.set_index('Neighborhood')

nyc_tor_merged.iloc[300:310] # check the last columns!

Examining clusters¶

Let's see the neighborhoods in each of the five clusters:

Cluster 1¶

c1 = nyc_tor_merged.loc[nyc_tor_merged['Cluster Labels'] == 0, :]
print(c1.shape)
c1.iloc[60:70]

(18, 8)

Most common categories among all neighborhoods in this cluster:

Cluster 2¶

c2 = nyc_tor_merged.loc[nyc_tor_merged['Cluster Labels'] == 1, :]
print(c2.shape)
c2

(43, 8)

Cluster 3¶

c3 = nyc_tor_merged.loc[nyc_tor_merged['Cluster Labels'] == 2, :]
print(c3.shape)
c3.tail()

(63, 8)

Cluster 4¶

c4 = nyc_tor_merged.loc[nyc_tor_merged['Cluster Labels'] == 3, :]
print(c4.shape)
c4

(103, 8)

Cluster 5¶

c5 = nyc_tor_merged.loc[nyc_tor_merged['Cluster Labels'] == 4, :]
print(c5.shape)
c5

(178, 8)

clust_cats = []
clusters = [c1, c2, c3, c4, c5]
for i, clus in enumerate(clusters):
    clust_cats.append([])
    for n in clus.index.values:
        if n.endswith('NYC'):
            nn = n[:-4]
            clust_cats[i].extend(list(nyc_venues[nyc_venues['Neighborhood'] == nn]['Venue Category'].values))
        else:
            nn = n[:-8]
            clust_cats[i].extend(list(tor_venues[tor_venues['Neighborhood'] == nn]['Venue Category'].values))
    clust_cats[i] = pd.Series(clust_cats[i]).value_counts(normalize=True) * 100

# clust_cats[0]

tbl_bck = "#363636"
tbl_hdr_bck = "#363636"
tbl_txt = "#BBBBBB"

styles = [
    dict(selector="td", props=[("border", "0px solid #333"), ("padding", "8px 20px 8px 20px"), 
                               ("background", tbl_bck), ("text-align", "left"),
                               ("color", tbl_txt), ("font-size", "10pt")]),
    
    dict(selector="th", props=[("border", "0px solid #333"), ("padding", "8px 20px 8px 20px"), 
                               ("background", tbl_bck), ("text-align", "left"),
                               ("color", tbl_txt), ("font-size", "10pt")]),
    
    dict(selector=".col0", props=[("border-left", "1px solid #bbbbbb")]), 
    
    dict(selector="th.blank:nth-child(2)", props=[("border-left", "1px solid #bbbbbb")]),
    
    dict(selector="thead tr:nth-child(2) th", props=[("padding-top", "0"), ("padding-bottom", "8px")]),
    
    dict(selector="th.col_heading", props=[("background", tbl_hdr_bck), 
                                           ("color", "#FFCA91")]),
    
    dict(selector="th.index_name", props=[("background", tbl_hdr_bck), 
                                          ("color", "#8DCDFF")]),
    
    dict(selector="th.blank", props=[("background", tbl_hdr_bck), ("padding", "0")]),
    
    dict(selector="tr:nth-child(2n)", props=[("background", "white")]),
    
    dict(selector="thead tr:nth-child(2) th", props=[("border-bottom", "1px solid {}".format(tbl_txt))]),
    
    dict(selector="thead tr:nth-child(1) th", props=[("padding-top", "14px !important")]),
    
    dict(selector="tbody tr:last-child td", props=[("padding-bottom", "20px")]),
    
    dict(selector="tbody tr:last-child th", props=[("padding-bottom", "20px")]),
    
    dict(selector="td:hover", props=[("font-weight", "bold"), ("background", "#002b36"), ("color", "#8DCDFF")]),
]

for i in range(kclusters):
    c__ = clust_cats[i].to_frame("% of venues")
    c__.index.names = ['Category']
    html = (c__.head(7).style.set_table_styles(styles)
            .set_table_attributes('style="border-collapse: collapse; border: 2px solid #BBBBBB"'))
    display(HTML(disp_fmt.format("Cluster {}".format(i+1))))
    display(html)

n_counts = []
for c in clusters:
    nyc_c = 0
    tor_c = 0
    for n in c.index.values:
        if n.endswith('NYC'):
            nyc_c += 1
        else:
            tor_c += 1
    n_counts.append((nyc_c, tor_c))

ind = ['cluster 1', 'cluster 2', 'cluster 3', 'cluster 4', 'cluster 5']
city_c_df = pd.DataFrame({
    'NYC': [x[0] for x in n_counts],
    'Toronto': [x[1] for x in n_counts]
}, index = ind)

fig, ax = plt.subplots(figsize=fig_size, facecolor=fig_fc)
city_c_df.plot(kind='bar', color=['#2d1e86', '#f6962b'], rot=0, ax=ax)
plot_conf(ax, xlbl='', ylbl='Number of neighborhoods', t='')
fig.savefig('city-count.png', dpi=300)

Discussion¶

Our analysis shows that NYC and Toronto have the same Top 2 common venue categories: apartments and barbershops.

The Top 10 Common Venue Categories suggest what types of businesses are more likely to thrive.

The Rare Categories suggest what types of businesses are less desirable.

Conclusion¶

The neighborhoods of New York City and Toronto were clustered into multiple groups based on the categories of the venues in these neighborhoods. The results show that there are venue categories that are more common in some cluster than the others.

	Neighborhood	Dessert Shop	Food	Gas Station	Salon / Barbershop
0	Wakefield	0	0	1	0
1	Wakefield	0	1	0	0
2	Wakefield	0	0	0	1
3	Wakefield	1	0	0	0
5	Wakefield	0	0	1	0

	Postal Code	Latitude	Longitude
0	M1B	43.806686	-79.194353
1	M1C	43.784535	-79.160497
2	M1E	43.763573	-79.188711
3	M1G	43.770992	-79.216917
4	M1H	43.773136	-79.239476

	Neighborhood	Neighborhood Latitude	Neighborhood Longitude	Venue	Venue Latitude	Venue Longitude	Venue Category
0	Malvern, Rouge	43.806686	-79.194353	Rouge Park - Woodland Trail	43.801782	-79.200427	Trail
1	Malvern, Rouge	43.806686	-79.194353	Alvin Curling Public School	43.808683	-79.190103	Elementary School
2	Malvern, Rouge	43.806686	-79.194353	Shell	43.803227	-79.192414	Gas Station
4	Malvern, Rouge	43.806686	-79.194353	Pleasant Corner	43.801164	-79.200254	Shopping Mall
5	Malvern, Rouge	43.806686	-79.194353	FASTSIGNS	43.807882	-79.201968	Business Service

	Neighborhood	Business Service	Elementary School	Gas Station	Shopping Mall	Trail
0	Malvern, Rouge	0	0	0	0	1
1	Malvern, Rouge	0	1	0	0	0
2	Malvern, Rouge	0	0	1	0	0
4	Malvern, Rouge	0	0	0	1	0
5	Malvern, Rouge	1	0	0	0	0

	Count
Venue Category
Colombian Restaurant	1
Light Rail Station	1
Line / Queue	1
Hunan Restaurant	1
Costume Shop	1
Roller Rink	1
Cable Car	1
Caucasian Restaurant	1
Forest	1
Adult Boutique	1

	Borough	Neighborhood	Latitude	Longitude
0	Bronx	Wakefield	40.894705	-73.847201
1	Bronx	Co-op City	40.874294	-73.829939
2	Bronx	Eastchester	40.887556	-73.827806
3	Bronx	Fieldston	40.895437	-73.905643
4	Bronx	Riverdale	40.890834	-73.912585

	Borough	Neighborhood	Latitude	Longitude
175	Queens	Bay Terrace	40.782843	-73.776802
235	Staten Island	Bay Terrace	40.553988	-74.139166

	Borough	Neighborhood	Latitude	Longitude
175	Queens	Bay Terrace, Queens	40.782843	-73.776802
235	Staten Island	Bay Terrace, Staten Island	40.553988	-74.139166

	Neighborhood	American Restaurant	Arcade	Athletics & Sports	Automotive Shop	Bagel Shop	Bakery	Bank	Bar	Beach	Bed & Breakfast	Bike Trail	Boat or Ferry	Breakfast Spot	Brewery	Bridal Shop	Bridge	Butcher	Café	Candy Store	Car Wash	Caribbean Restaurant	Chinese Restaurant	Chiropractor	Church	Clothing Store	Club House	Cocktail Bar	Coffee Shop	College Classroom	Conference Room	Convenience Store	Convention Center	Cosmetics Shop	Dance Studio	Daycare	Deli / Bodega	Dentist's Office	Department Store	Diner	Discount Store	Doctor's Office	Donut Shop	Dry Cleaner	Electronics Store	Elementary School	Event Space	Fast Food Restaurant	Field	Financial or Legal Service	Fire Station	Flower Shop	Food	Food Service	Food Truck	Fraternity House	Fried Chicken Joint	Funeral Home	Furniture / Home Store	Gaming Cafe	Garden	Gas Station	Gay Bar	General Entertainment	General Travel	Gift Shop	Government Building	Grocery Store	Gym	Hardware Store	Health & Beauty Service	High School	Home Service	Hookah Bar	Housing Development	Ice Cream Shop	Indie Theater	Intersection	Island	Italian Restaurant	Jewelry Store	Juice Bar	Latin American Restaurant	Laundry Service	Lawyer	Library	Liquor Store	Lounge	Market	Martial Arts School	Medical Center	Mediterranean Restaurant	Mexican Restaurant	Middle Eastern Restaurant	Military Base	Miscellaneous Shop	Mobile Phone Shop	Monument / Landmark	Mosque	Motorcycle Shop	Movie Theater	Moving Target	Nail Salon	Nightclub	Non-Profit	Nursery School	Optical Shop	Other Great Outdoors	Other Nightlife	Outdoors & Recreation	Pakistani Restaurant	Paper / Office Supplies Store	Park	Parking	Peruvian Restaurant	Pet Service	Pet Store	Pharmacy	Pizza Place	Playground	Polish Restaurant	Pool	Professional & Other Places	Pub	Residence	Residential Building (Apartment / Condo)	Restaurant	Salon / Barbershop	Sandwich Place	Scenic Lookout	School	Shopping Mall	Smoke Shop	Snack Place	Social Club	Spa	Spiritual Center	Sporting Goods Shop	Stationery Store	Storage Facility	Supermarket	Sushi Restaurant	Synagogue	Taco Place	Tattoo Parlor	Taxi	Track	Trail	Train Station	University	Urgent Care Center	Veterinarian	Warehouse	Wine Bar
0	Allerton	0.000000	0.000000	0.000000	0.023810	0.000000	0.000000	0.000000	0.000000	0.000000	0.011905	0.011905	0.000000	0.011905	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.035714	0.000000	0.023810	0.02381	0.000000	0.011905	0.000000	0.011905	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.023810	0.011905	0.000000	0.000000	0.011905	0.023810	0.011905	0.011905	0.02381	0.000000	0.000000	0.011905	0.000000	0.023810	0.000000	0.000000	0.035714	0.000000	0.000000	0.000000	0.011905	0.023810	0.000000	0.000000	0.000000	0.035714	0.000000	0.000000	0.000000	0.000000	0.000000	0.011905	0.000000	0.000000	0.011905	0.000000	0.011905	0.011905	0.011905	0.011905	0.000000	0.000000	0.000000	0.000000	0.011905	0.000000	0.000000	0.059524	0.011905	0.011905	0.011905	0.011905	0.011905	0.011905	0.011905	0.000000	0.000000	0.000000	0.000000	0.011905	0.000000	0.000000	0.000000	0.011905	0.000000	0.000000	0.000000	0.000000	0.035714	0.011905	0.000000	0.011905	0.000000	0.011905	0.011905	0.011905	0.000000	0.011905	0.000000	0.000000	0.000000	0.035714	0.035714	0.011905	0.000000	0.000000	0.000000	0.000000	0.000000	0.011905	0.000000	0.071429	0.000000	0.000000	0.011905	0.000000	0.000000	0.000000	0.000000	0.023810	0.000000	0.000000	0.000000	0.000000	0.011905	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
1	Annadale	0.038961	0.000000	0.000000	0.012987	0.025974	0.025974	0.012987	0.025974	0.012987	0.000000	0.000000	0.000000	0.000000	0.000000	0.012987	0.000000	0.025974	0.000000	0.012987	0.012987	0.000000	0.000000	0.00000	0.000000	0.000000	0.012987	0.012987	0.000000	0.012987	0.000000	0.000000	0.012987	0.012987	0.012987	0.000000	0.012987	0.012987	0.000000	0.012987	0.000000	0.025974	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.012987	0.012987	0.000000	0.012987	0.012987	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.025974	0.000000	0.025974	0.000000	0.000000	0.000000	0.000000	0.012987	0.012987	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.012987	0.000000	0.000000	0.000000	0.012987	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.012987	0.000000	0.000000	0.000000	0.000000	0.038961	0.000000	0.000000	0.000000	0.000000	0.000000	0.012987	0.000000	0.000000	0.000000	0.012987	0.000000	0.000000	0.000000	0.025974	0.012987	0.077922	0.000000	0.000000	0.025974	0.000000	0.012987	0.000000	0.000000	0.025974	0.077922	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.025974	0.000000	0.000000	0.012987	0.000000	0.000000	0.012987	0.000000	0.000000	0.038961	0.000000	0.000000	0.012987	0.012987	0.000000	0.000000	0.025974	0.000000	0.000000
2	Arden Heights	0.000000	0.000000	0.000000	0.000000	0.014493	0.000000	0.014493	0.028986	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.028986	0.000000	0.000000	0.000000	0.000000	0.000000	0.014493	0.00000	0.043478	0.000000	0.000000	0.028986	0.000000	0.000000	0.014493	0.000000	0.000000	0.000000	0.000000	0.000000	0.014493	0.043478	0.000000	0.000000	0.000000	0.043478	0.000000	0.000000	0.00000	0.014493	0.000000	0.000000	0.014493	0.000000	0.000000	0.000000	0.028986	0.000000	0.014493	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.028986	0.000000	0.014493	0.000000	0.000000	0.028986	0.000000	0.000000	0.000000	0.000000	0.014493	0.014493	0.000000	0.014493	0.000000	0.000000	0.028986	0.000000	0.000000	0.000000	0.014493	0.000000	0.000000	0.014493	0.000000	0.000000	0.000000	0.028986	0.000000	0.014493	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.028986	0.014493	0.000000	0.000000	0.000000	0.014493	0.028986	0.000000	0.014493	0.000000	0.000000	0.014493	0.000000	0.000000	0.000000	0.000000	0.014493	0.014493	0.014493	0.000000	0.000000	0.057971	0.014493	0.000000	0.000000	0.000000	0.014493	0.014493	0.000000	0.014493	0.014493	0.014493	0.000000	0.000000	0.000000	0.014493	0.000000	0.000000	0.000000	0.014493	0.014493	0.014493	0.000000	0.000000	0.014493	0.014493	0.000000	0.000000	0.000000	0.014493	0.000000	0.000000	0.014493
3	Arlington	0.027778	0.013889	0.000000	0.041667	0.013889	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.013889	0.000000	0.013889	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.013889	0.000000	0.00000	0.097222	0.000000	0.000000	0.000000	0.013889	0.000000	0.000000	0.013889	0.000000	0.000000	0.000000	0.013889	0.027778	0.013889	0.013889	0.000000	0.000000	0.013889	0.000000	0.000000	0.00000	0.013889	0.000000	0.000000	0.000000	0.013889	0.013889	0.000000	0.000000	0.013889	0.000000	0.013889	0.000000	0.000000	0.013889	0.000000	0.000000	0.000000	0.013889	0.013889	0.000000	0.000000	0.000000	0.000000	0.000000	0.055556	0.000000	0.000000	0.013889	0.000000	0.027778	0.000000	0.000000	0.013889	0.013889	0.000000	0.000000	0.013889	0.000000	0.027778	0.000000	0.013889	0.000000	0.013889	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.013889	0.000000	0.000000	0.000000	0.013889	0.000000	0.013889	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.013889	0.000000	0.013889	0.000000	0.000000	0.000000	0.013889	0.027778	0.013889	0.000000	0.041667	0.000000	0.013889	0.041667	0.000000	0.041667	0.000000	0.013889	0.013889	0.000000	0.000000	0.013889	0.000000	0.000000	0.000000	0.000000	0.000000	0.013889	0.013889	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.013889	0.000000
4	Arrochar	0.000000	0.000000	0.012987	0.000000	0.025974	0.000000	0.000000	0.025974	0.025974	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.012987	0.012987	0.000000	0.000000	0.025974	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.012987	0.025974	0.000000	0.051948	0.012987	0.000000	0.000000	0.000000	0.025974	0.000000	0.025974	0.00000	0.012987	0.025974	0.000000	0.000000	0.000000	0.012987	0.000000	0.025974	0.000000	0.038961	0.000000	0.000000	0.012987	0.000000	0.012987	0.012987	0.000000	0.000000	0.000000	0.012987	0.000000	0.012987	0.000000	0.000000	0.000000	0.000000	0.012987	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.025974	0.000000	0.000000	0.012987	0.025974	0.012987	0.012987	0.025974	0.000000	0.000000	0.000000	0.012987	0.012987	0.000000	0.012987	0.000000	0.012987	0.012987	0.000000	0.000000	0.000000	0.012987	0.000000	0.025974	0.012987	0.000000	0.000000	0.000000	0.000000	0.000000	0.012987	0.000000	0.000000	0.012987	0.000000	0.000000	0.012987	0.000000	0.012987	0.038961	0.012987	0.012987	0.000000	0.000000	0.000000	0.000000	0.000000	0.025974	0.025974	0.012987	0.012987	0.000000	0.000000	0.000000	0.000000	0.012987	0.012987	0.000000	0.012987	0.000000	0.000000	0.012987	0.000000	0.000000	0.012987	0.000000	0.000000	0.000000	0.000000	0.000000	0.012987	0.000000	0.000000	0.000000	0.000000

	Neighborhood	1st Most Common Category	2nd Most Common Category	3rd Most Common Category	4th Most Common Category	5th Most Common Category	6th Most Common Category	7th Most Common Category	8th Most Common Category	9th Most Common Category	10th Most Common Category
8	Norwood	Doctor's Office	Park	Deli / Bodega	Residential Building (Apartment / Condo)	Medical Center	Food Truck	School	Salon / Barbershop	Laundry Service	Middle School
11	Pelham Parkway	Doctor's Office	Deli / Bodega	Residential Building (Apartment / Condo)	Dentist's Office	Salon / Barbershop	Diner	Pizza Place	Food	Flower Shop	Café
33	Morris Park	Doctor's Office	Residential Building (Apartment / Condo)	Nail Salon	Dentist's Office	Ice Cream Shop	Chinese Restaurant	Church	Salon / Barbershop	Restaurant	Real Estate Office
38	Schuylerville	Doctor's Office	Salon / Barbershop	Deli / Bodega	Professional & Other Places	Pizza Place	Pharmacy	Mental Health Office	Medical Lab	Hardware Store	Donut Shop
42	Pelham Gardens	Doctor's Office	Pharmacy	Pizza Place	Playground	Other Great Outdoors	Dentist's Office	Gas Station	Sandwich Place	School	Deli / Bodega
53	Manhattan Terrace	Doctor's Office	Dentist's Office	Residential Building (Apartment / Condo)	Parking	Housing Development	School	Medical Center	Professional & Other Places	Café	Chinese Restaurant
64	Brooklyn Heights	Doctor's Office	Residential Building (Apartment / Condo)	School	Women's Store	Church	Salon / Barbershop	Laundry Service	Flea Market	Monument / Landmark	Health & Beauty Service
73	Starrett City	Doctor's Office	Laundry Service	Parking	Residential Building (Apartment / Condo)	Pharmacy	Middle School	Gym / Fitness Center	Salon / Barbershop	School	College Academic Building
81	Dyker Heights	Doctor's Office	Salon / Barbershop	Gas Station	Dog Run	Event Space	Automotive Shop	Deli / Bodega	Bagel Shop	Emergency Room	Pet Store
94	Georgetown	Doctor's Office	Bank	Medical Center	Chinese Restaurant	Salon / Barbershop	Gas Station	Medical Lab	Supermarket	Laundry Service	Mobile Phone Shop
99	Fort Hamilton	Doctor's Office	Residential Building (Apartment / Condo)	Pizza Place	Deli / Bodega	Laundry Service	Salon / Barbershop	Miscellaneous Shop	Diner	Automotive Shop	Chinese Restaurant
107	Upper East Side	Doctor's Office	Art Gallery	Residential Building (Apartment / Condo)	Medical Center	Dentist's Office	Library	Coffee Shop	Spa	High School	Sculpture Garden
115	Murray Hill, Manhattan	Doctor's Office	Residential Building (Apartment / Condo)	Dentist's Office	Event Space	Embassy / Consulate	Spa	Hotel	Parking	Gym	Medical Center
126	Gramercy	Doctor's Office	Residential Building (Apartment / Condo)	Medical Center	Salon / Barbershop	Dentist's Office	Laundry Service	Beer Garden	College Arts Building	Gourmet Shop	Pharmacy
135	Forest Hills	Dentist's Office	Doctor's Office	Residential Building (Apartment / Condo)	Bank	School	Tech Startup	Professional & Other Places	Library	Elementary School	Convention Center
144	Glendale	Doctor's Office	Deli / Bodega	Salon / Barbershop	Chinese Restaurant	Church	Event Space	Pizza Place	Basketball Court	School	Park
145	Rego Park	Doctor's Office	Residential Building (Apartment / Condo)	Medical Center	Dentist's Office	Professional & Other Places	Indian Restaurant	Spa	General College & University	Mediterranean Restaurant	Bank
182	Holliswood	Doctor's Office	Gas Station	Residential Building (Apartment / Condo)	Salon / Barbershop	Dentist's Office	Bank	Event Space	Bagel Shop	Professional & Other Places	Donut Shop
187	Lindenwood	Doctor's Office	Dentist's Office	Residential Building (Apartment / Condo)	Bank	Miscellaneous Shop	Medical Center	Deli / Bodega	Weight Loss Center	School	Laundry Service
203	Todt Hill	Doctor's Office	Professional & Other Places	Other Great Outdoors	Bagel Shop	Salon / Barbershop	Dog Run	Park	Dentist's Office	School	New American Restaurant
204	South Beach	Hospital	Medical Center	Doctor's Office	Beach	Dentist's Office	BBQ Joint	Gym	College Communications Building	College Auditorium	Emergency Room
208	Castleton Corners	Doctor's Office	Church	Dentist's Office	Deli / Bodega	Salon / Barbershop	Medical Center	Bakery	Nail Salon	Chiropractor	Miscellaneous Shop
209	New Springville	Doctor's Office	Salon / Barbershop	Bagel Shop	Mobile Phone Shop	Deli / Bodega	Miscellaneous Shop	Martial Arts School	Flower Shop	Chinese Restaurant	Middle Eastern Restaurant
212	Oakwood	Doctor's Office	Dentist's Office	Church	Medical Center	Other Great Outdoors	Other Nightlife	Chiropractor	Elementary School	Strip Club	Martial Arts School
220	Sunnyside, Staten Island	Doctor's Office	Theater	College Residence Hall	Dance Studio	College Library	College Arts Building	College Administrative Building	College Lab	Playground	Indie Theater
231	Dongan Hills	Deli / Bodega	Doctor's Office	Salon / Barbershop	Bar	Church	Pet Store	Residential Building (Apartment / Condo)	Parking	Garden	Bowling Alley
242	Greenridge	Doctor's Office	Dentist's Office	Professional & Other Places	Asian Restaurant	Taxi	Laundry Service	Fire Station	Parking	Bowling Alley	Smoke Shop
243	Heartland Village	Doctor's Office	Salon / Barbershop	Pizza Place	Chinese Restaurant	Dentist's Office	Bagel Shop	Professional & Other Places	Cosmetics Shop	Miscellaneous Shop	Middle Eastern Restaurant
245	Bloomfield	Doctor's Office	Automotive Shop	Baseball Field	Sporting Goods Shop	Grocery Store	Other Great Outdoors	General College & University	Miscellaneous Shop	Business Center	Cosmetics Shop
246	Bulls Head	Doctor's Office	Pizza Place	Bank	Dentist's Office	Church	Cosmetics Shop	Hospital	Chinese Restaurant	Salon / Barbershop	Café
254	Concord	Doctor's Office	Deli / Bodega	Bakery	Salon / Barbershop	Peruvian Restaurant	Martial Arts School	Medical Center	Automotive Shop	Other Great Outdoors	Chinese Restaurant
255	Emerson Hill	Doctor's Office	Medical Center	Church	Residential Building (Apartment / Condo)	College Library	Spa	College Administrative Building	Lounge	Scenic Lookout	Market
256	Randall Manor	Doctor's Office	High School	Residential Building (Apartment / Condo)	Park	College Classroom	Medical Center	Deli / Bodega	Automotive Shop	Liquor Store	Pizza Place
265	Pomonok	Doctor's Office	Residential Building (Apartment / Condo)	Playground	Elementary School	Salon / Barbershop	Dentist's Office	Library	Other Nightlife	Park	Medical Center
274	Tudor City	Doctor's Office	Residential Building (Apartment / Condo)	Medical Center	Gym	Laundry Service	Hospital	Veterinarian	Train Station	Assisted Living	Lawyer
287	Egbertville	Doctor's Office	Salon / Barbershop	Automotive Shop	Trail	Park	Dentist's Office	Scenic Lookout	Lake	Pizza Place	Deli / Bodega
296	Madison	Doctor's Office	Residential Building (Apartment / Condo)	Synagogue	Dentist's Office	Laundry Service	High School	Spa	Student Center	Food	Medical Center

	PostalCode	Borough	Neighborhood
0	M1A	Not assigned	Not assigned
1	M2A	Not assigned	Not assigned
2	M3A	North York	Parkwoods
3	M4A	North York	Victoria Village
4	M5A	Downtown Toronto	Regent Park, Harbourfront

	PostalCode	Borough	Neighborhood	Latitude	Longitude
0	M1B	Scarborough	Malvern, Rouge	43.806686	-79.194353
1	M1C	Scarborough	Rouge Hill, Port Union, Highland Creek	43.784535	-79.160497
2	M1E	Scarborough	Guildwood, Morningside, West Hill	43.763573	-79.188711
3	M1G	Scarborough	Woburn	43.770992	-79.216917
4	M1H	Scarborough	Cedarbrae	43.773136	-79.239476

	Neighborhood	Acupuncturist	Arcade	Art Gallery	Art Studio	Arts & Crafts Store	Asian Restaurant	Assisted Living	Athletics & Sports	Auditorium	Auto Dealership	Auto Garage	Auto Workshop	Automotive Shop	BBQ Joint	Badminton Court	Bakery	Bank	Bar	Bike Shop	Bistro	Boutique	Breakfast Spot	Bridal Shop	Burger Joint	Burmese Restaurant	Business Service	Café	Capitol Building	Car Wash	Caribbean Restaurant	Chinese Restaurant	Chiropractor	Church	Clothing Store	Coffee Shop	College Classroom	College Library	Comfort Food Restaurant	Concert Hall	Conference Room	Convenience Store	Cosmetics Shop	Coworking Space	Cupcake Shop	Dance Studio	Daycare	Deli / Bodega	Dentist's Office	Design Studio	Diner	Doctor's Office	Dog Run	Electronics Store	Escape Room	Event Space	Factory	Fast Food Restaurant	Field	Flower Shop	Fried Chicken Joint	Frozen Yogurt Shop	Furniture / Home Store	Garden Center	Gas Station	General Entertainment	General Travel	Gift Shop	Gluten-free Restaurant	Government Building	Grocery Store	Gym	Gym / Fitness Center	Hardware Store	Health & Beauty Service	Health Food Store	High School	Hobby Shop	Home Service	Hotel	Hungarian Restaurant	Ice Cream Shop	Indian Restaurant	Intersection	Italian Restaurant	Japanese Restaurant	Jewelry Store	Jewish Restaurant	Juice Bar	Korean Restaurant	Latin American Restaurant	Laundromat	Laundry Service	Lawyer	Library	Lingerie Store	Liquor Store	Massage Studio	Mattress Store	Medical Center	Medical School	Medical Supply Store	Men's Store	Middle Eastern Restaurant	Miscellaneous Shop	Mobile Phone Shop	Monument / Landmark	Motorcycle Shop	Music Venue	Nail Salon	New American Restaurant	Newsagent	Newsstand	Non-Profit	Optical Shop	Other Nightlife	Park	Pet Service	Pet Store	Pharmacy	Pizza Place	Playground	Pool	Post Office	Print Shop	Professional & Other Places	Pub	Public Art	Recreation Center	Rehab Center	Rental Car Location	Rental Service	Residential Building (Apartment / Condo)	Restaurant	Salon / Barbershop	Sandwich Place	School	Shoe Store	Shop & Service	Shopping Mall	Shopping Plaza	Skate Park	Skating Rink	Spa	Sporting Goods Shop	State / Provincial Park	Storage Facility	Supermarket	Sushi Restaurant	Synagogue	Tailor Shop	Tech Startup	Thai Restaurant	Trail	Train Station	Veterinarian	Vietnamese Restaurant	Warehouse	Wings Joint	Yoga Studio
0	Agincourt	0.000000	0.0125	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.025000	0.025	0.0125	0.175000	0.000000	0.0125	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.012500	0.000000	0.000000	0.000000	0.025000	0.000000	0.012500	0.000000	0.000000	0.025000	0.0125	0.037500	0.0125	0.037500	0.0125	0.000000	0.000000	0.000000	0.000000	0.012500	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.012500	0.000000	0.000000	0.0125	0.025000	0.025	0.000000	0.000000	0.000000	0.000000	0.000000	0.025000	0.000000	0.025000	0.025000	0.000000	0.000000	0.000000	0.012500	0.012500	0.012500	0.000000	0.025000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.012500	0.000000	0.000000	0.000000	0.000000	0.000000	0.012500	0.0125	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0125	0.012500	0.000000	0.0125	0.000000	0.000000	0.012500	0.000000	0.000000	0.0125	0.000000	0.000000	0.000000	0.0125	0.0125	0.012500	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0375	0.0125	0.000000	0.000000	0.000000	0.0125	0.000000	0.025	0.000000	0.000000	0.000000	0.012500	0.012500	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0125	0.000000	0.0375	0.000000	0.000000	0.00000	0.000000	0.012500	0.000000	0.000000	0.0125	0.000000	0.000000	0.000000	0.000000	0.000000
1	Alderwood, Long Branch	0.000000	0.0000	0.000000	0.012346	0.012346	0.012346	0.000000	0.012346	0.000000	0.012346	0.000	0.0000	0.012346	0.024691	0.0000	0.000000	0.037037	0.000000	0.012346	0.012346	0.000000	0.012346	0.000000	0.012346	0.012346	0.000000	0.024691	0.000000	0.000000	0.000000	0.000000	0.0000	0.000000	0.0000	0.012346	0.0000	0.000000	0.000000	0.000000	0.037037	0.037037	0.000000	0.012346	0.000000	0.012346	0.037037	0.012346	0.037037	0.012346	0.012346	0.012346	0.000000	0.000000	0.0000	0.000000	0.000	0.000000	0.000000	0.000000	0.000000	0.000000	0.012346	0.000000	0.037037	0.012346	0.000000	0.000000	0.000000	0.000000	0.000000	0.012346	0.012346	0.012346	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.012346	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0000	0.012346	0.012346	0.012346	0.012346	0.012346	0.000000	0.000000	0.0000	0.037037	0.000000	0.0000	0.000000	0.000000	0.012346	0.000000	0.000000	0.0000	0.000000	0.012346	0.012346	0.0000	0.0000	0.012346	0.000000	0.000000	0.000000	0.000000	0.000000	0.012346	0.024691	0.012346	0.012346	0.0000	0.0000	0.012346	0.024691	0.000000	0.0000	0.000000	0.000	0.012346	0.000000	0.000000	0.037037	0.012346	0.012346	0.012346	0.012346	0.000000	0.000000	0.000000	0.000000	0.037037	0.0000	0.012346	0.0000	0.000000	0.000000	0.00000	0.000000	0.012346	0.012346	0.000000	0.0000	0.000000	0.000000	0.012346	0.000000	0.000000
2	Bathurst Manor, Wilson Heights, Downsview North	0.000000	0.0000	0.012195	0.000000	0.000000	0.000000	0.000000	0.000000	0.012195	0.000000	0.000	0.0000	0.012195	0.000000	0.0000	0.000000	0.048780	0.012195	0.000000	0.000000	0.000000	0.000000	0.012195	0.000000	0.000000	0.000000	0.000000	0.012195	0.012195	0.000000	0.000000	0.0000	0.012195	0.0000	0.024390	0.0000	0.000000	0.000000	0.000000	0.000000	0.024390	0.012195	0.000000	0.000000	0.000000	0.000000	0.012195	0.012195	0.000000	0.012195	0.085366	0.000000	0.000000	0.0000	0.000000	0.000	0.000000	0.000000	0.000000	0.012195	0.012195	0.000000	0.000000	0.012195	0.000000	0.000000	0.012195	0.000000	0.012195	0.012195	0.000000	0.000000	0.000000	0.000000	0.012195	0.000000	0.000000	0.000000	0.012195	0.000000	0.036585	0.000000	0.012195	0.000000	0.000000	0.000000	0.000000	0.000000	0.012195	0.0000	0.000000	0.036585	0.000000	0.000000	0.000000	0.000000	0.000000	0.0000	0.060976	0.012195	0.0000	0.000000	0.012195	0.012195	0.012195	0.012195	0.0000	0.000000	0.012195	0.000000	0.0000	0.0000	0.012195	0.000000	0.000000	0.012195	0.000000	0.012195	0.012195	0.012195	0.012195	0.000000	0.0000	0.0000	0.000000	0.000000	0.000000	0.0000	0.000000	0.000	0.000000	0.060976	0.012195	0.036585	0.012195	0.000000	0.000000	0.000000	0.012195	0.000000	0.000000	0.000000	0.036585	0.0000	0.000000	0.0000	0.012195	0.012195	0.04878	0.000000	0.000000	0.000000	0.000000	0.0000	0.012195	0.000000	0.000000	0.000000	0.012195
3	Bayview Village	0.012658	0.0000	0.000000	0.000000	0.000000	0.012658	0.012658	0.000000	0.000000	0.000000	0.000	0.0000	0.012658	0.000000	0.0000	0.000000	0.037975	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.025316	0.000000	0.000000	0.000000	0.012658	0.0000	0.063291	0.0000	0.012658	0.0000	0.012658	0.000000	0.012658	0.000000	0.012658	0.000000	0.012658	0.000000	0.012658	0.012658	0.000000	0.012658	0.000000	0.000000	0.063291	0.037975	0.012658	0.0000	0.012658	0.000	0.000000	0.012658	0.012658	0.000000	0.000000	0.000000	0.000000	0.025316	0.000000	0.000000	0.000000	0.000000	0.000000	0.037975	0.000000	0.000000	0.000000	0.000000	0.000000	0.012658	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.025316	0.000000	0.025316	0.000000	0.000000	0.000000	0.012658	0.0000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.012658	0.0000	0.025316	0.000000	0.0000	0.000000	0.012658	0.000000	0.000000	0.000000	0.0000	0.012658	0.012658	0.000000	0.0000	0.0000	0.000000	0.025316	0.000000	0.025316	0.012658	0.000000	0.025316	0.000000	0.025316	0.000000	0.0000	0.0000	0.000000	0.000000	0.000000	0.0000	0.012658	0.000	0.000000	0.025316	0.012658	0.063291	0.000000	0.025316	0.000000	0.000000	0.012658	0.012658	0.012658	0.012658	0.000000	0.0000	0.000000	0.0000	0.000000	0.012658	0.00000	0.000000	0.000000	0.000000	0.012658	0.0000	0.012658	0.000000	0.000000	0.000000	0.000000
4	Bedford Park, Lawrence Manor East	0.000000	0.0000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000	0.0000	0.000000	0.000000	0.0000	0.011494	0.000000	0.000000	0.011494	0.000000	0.022989	0.011494	0.011494	0.000000	0.000000	0.011494	0.011494	0.000000	0.000000	0.011494	0.000000	0.0000	0.011494	0.0000	0.022989	0.0000	0.000000	0.011494	0.000000	0.000000	0.011494	0.022989	0.000000	0.022989	0.000000	0.000000	0.000000	0.022989	0.000000	0.000000	0.000000	0.000000	0.011494	0.0000	0.000000	0.000	0.011494	0.000000	0.000000	0.000000	0.000000	0.022989	0.011494	0.022989	0.000000	0.011494	0.011494	0.011494	0.000000	0.000000	0.000000	0.000000	0.011494	0.011494	0.000000	0.000000	0.022989	0.011494	0.000000	0.000000	0.011494	0.011494	0.000000	0.057471	0.011494	0.011494	0.011494	0.011494	0.000000	0.0000	0.000000	0.000000	0.000000	0.000000	0.000000	0.022989	0.000000	0.0000	0.034483	0.000000	0.0000	0.011494	0.000000	0.011494	0.022989	0.000000	0.0000	0.000000	0.022989	0.000000	0.0000	0.0000	0.000000	0.000000	0.011494	0.000000	0.011494	0.011494	0.000000	0.011494	0.000000	0.000000	0.0000	0.0000	0.000000	0.011494	0.011494	0.0000	0.000000	0.000	0.000000	0.000000	0.022989	0.103448	0.022989	0.011494	0.011494	0.000000	0.000000	0.000000	0.000000	0.000000	0.022989	0.0000	0.000000	0.0000	0.000000	0.034483	0.00000	0.011494	0.000000	0.011494	0.000000	0.0000	0.000000	0.011494	0.000000	0.011494	0.000000

	Neighborhood	Acupuncturist	American Restaurant	Arcade	Art Gallery	Art Studio	Arts & Crafts Store	Asian Restaurant	Athletics & Sports	ATM	Auditorium	Auto Dealership	Auto Garage	Auto Workshop	Automotive Shop	Badminton Court	Bagel Shop	Bakery	Bank	Bar	Basketball Court	BBQ Joint	Beer Garden	Bike Rental / Bike Share	Bike Shop	Bistro	Breakfast Spot	Bridal Shop	Burger Joint	Burmese Restaurant	Business Service	Café	Campaign Office	Capitol Building	Car Wash	Chinese Restaurant	Chiropractor	Church	Clothing Store	Cocktail Bar	Coffee Shop	College Classroom	Community Center	Conference Room	Construction & Landscaping	Convenience Store	Cosmetics Shop	Coworking Space	Dance Studio	Daycare	Deli / Bodega	Dentist's Office	Design Studio	Diner	Doctor's Office	Dog Run	Donut Shop	Driving School	Dry Cleaner	Dumpling Restaurant	Electronics Store	Embassy / Consulate	Escape Room	Event Space	Factory	Farmers Market	Fast Food Restaurant	Financial or Legal Service	Fish & Chips Shop	Flower Shop	Food	Food & Drink Shop	Food Stand	Food Truck	Fried Chicken Joint	Frozen Yogurt Shop	Fruit & Vegetable Store	Furniture / Home Store	Gas Station	Gay Bar	General College & University	General Entertainment	General Travel	Gift Shop	Government Building	Grocery Store	Gym	Gym / Fitness Center	Hardware Store	Health & Beauty Service	Health Food Store	Himalayan Restaurant	Home Service	Hookah Bar	Hot Dog Joint	Hotel	Housing Development	Hungarian Restaurant	Ice Cream Shop	Indian Restaurant	Insurance Office	Intersection	Italian Restaurant	Juice Bar	Korean Restaurant	Language School	Latin American Restaurant	Laundromat	Laundry Service	Lawyer	Library	Lingerie Store	Liquor Store	Mac & Cheese Joint	Martial Arts School	Mattress Store	Medical Center	Medical School	Medical Supply Store	Mediterranean Restaurant	Metro Station	Mexican Restaurant	Middle Eastern Restaurant	Middle School	Miscellaneous Shop	Mobile Phone Shop	Monument / Landmark	Mosque	Motorcycle Shop	Mountain	Nail Salon	New American Restaurant	Newsagent	Newsstand	Nightclub	Nightlife Spot	Non-Profit	Other Great Outdoors	Park	Parking	Performing Arts Venue	Pet Store	Pharmacy	Physical Therapist	Pizza Place	Platform	Playground	Police Station	Pool	Post Office	Print Shop	Professional & Other Places	Pub	Recreation Center	Rental Car Location	Rental Service	Residential Building (Apartment / Condo)	Restaurant	Salon / Barbershop	Sandwich Place	School	Shoe Store	Shop & Service	Shopping Mall	Smoothie Shop	Spa	Sporting Goods Shop	State / Provincial Park	Storage Facility	Strip Club	Supermarket	Sushi Restaurant	Synagogue	Tanning Salon	Taxi	Tech Startup	Tennis Court	Thai Restaurant	Thrift / Vintage Store	Train	Train Station	Veterinarian	Warehouse	Wine Shop	Yoga Studio
303	Woodrow_NYC	0.000000	0.0125	0.0000	0.012500	0.000000	0.000000	0.000000	0.012500	0.00000	0.000000	0.000000	0.000	0.0000	0.012500	0.0000	0.012500	0.012500	0.012500	0.025000	0.0125	0.000000	0.0125	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000	0.012500	0.000000	0.000000	0.000000	0.0125	0.000000	0.025000	0.0000	0.025	0.012500	0.0000	0.00000	0.000000	0.000000	0.000000	0.025000	0.000000	0.000000	0.000000	0.000000	0.025000	0.000000	0.012500	0.012500	0.0125	0.0125	0.00000	0.000000	0.000000	0.0125	0.0125	0.0000	0.000000	0.000	0.0125	0.000000	0.00000	0.025	0.000000	0.01250	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.012500	0.0125	0.0125	0.000000	0.01250	0.025000	0.000000	0.037500	0.012500	0.000000	0.000000	0.000000	0.000000	0.00000	0.0125	0.0125	0.00000	0.000000	0.0125	0.000000	0.000000	0.00000	0.00000	0.000000	0.012500	0.0125	0.000000	0.00000	0.00000	0.000000	0.025000	0.000000	0.000000	0.000000	0.025000	0.0125	0.0125	0.0000	0.000000	0.000000	0.0000	0.000000	0.00000	0.012500	0.000000	0.0125	0.025000	0.012500	0.000000	0.00000	0.0000	0.00000	0.000000	0.000000	0.0000	0.00000	0.000000	0.000000	0.000000	0.012500	0.000000	0.000000	0.000000	0.000000	0.025000	0.025	0.012500	0.000000	0.000000	0.000000	0.050000	0.01250	0.0000	0.012500	0.000000	0.0000	0.000	0.000000	0.000000	0.012500	0.025000	0.000000	0.037500	0.000000	0.000000	0.000000	0.0125	0.000000	0.0000	0.000000	0.000000	0.0125	0.000000	0.012500	0.00000	0.0125	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.00000	0.000000	0.000000	0.000000	0.000000
304	Woodside_NYC	0.000000	0.0000	0.0000	0.000000	0.000000	0.000000	0.000000	0.000000	0.01087	0.000000	0.000000	0.000	0.0000	0.010870	0.0000	0.000000	0.021739	0.010870	0.065217	0.0000	0.000000	0.0000	0.000000	0.010870	0.000000	0.000000	0.000000	0.000000	0.000000	0.000	0.000000	0.000000	0.000000	0.000000	0.0000	0.000000	0.010870	0.0000	0.000	0.010870	0.0000	0.01087	0.000000	0.000000	0.010870	0.010870	0.000000	0.000000	0.000000	0.032609	0.021739	0.000000	0.010870	0.010870	0.0000	0.0000	0.01087	0.000000	0.000000	0.0000	0.0000	0.0000	0.010870	0.000	0.0000	0.021739	0.01087	0.000	0.000000	0.01087	0.000000	0.000000	0.021739	0.000000	0.000000	0.01087	0.000000	0.000000	0.0000	0.0000	0.010870	0.01087	0.000000	0.010870	0.021739	0.000000	0.000000	0.010870	0.021739	0.000000	0.01087	0.0000	0.0000	0.01087	0.000000	0.0000	0.000000	0.000000	0.01087	0.01087	0.000000	0.000000	0.0000	0.000000	0.01087	0.01087	0.010870	0.021739	0.000000	0.000000	0.000000	0.021739	0.0000	0.0000	0.0000	0.010870	0.000000	0.0000	0.000000	0.01087	0.054348	0.000000	0.0000	0.032609	0.000000	0.000000	0.01087	0.0000	0.01087	0.000000	0.000000	0.0000	0.01087	0.010870	0.000000	0.000000	0.021739	0.000000	0.000000	0.000000	0.000000	0.000000	0.000	0.010870	0.043478	0.000000	0.000000	0.000000	0.01087	0.0000	0.000000	0.000000	0.0000	0.000	0.000000	0.021739	0.000000	0.065217	0.000000	0.000000	0.000000	0.000000	0.000000	0.0000	0.010870	0.0000	0.000000	0.000000	0.0000	0.010870	0.000000	0.00000	0.0000	0.000000	0.000000	0.000000	0.043478	0.021739	0.01087	0.01087	0.000000	0.000000	0.000000	0.000000
305	Yorkville_NYC	0.011364	0.0000	0.0000	0.011364	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000	0.0000	0.000000	0.0000	0.011364	0.000000	0.011364	0.022727	0.0000	0.000000	0.0000	0.011364	0.011364	0.000000	0.000000	0.000000	0.000000	0.000000	0.000	0.000000	0.011364	0.000000	0.000000	0.0000	0.011364	0.000000	0.0000	0.000	0.000000	0.0000	0.00000	0.000000	0.011364	0.000000	0.000000	0.011364	0.000000	0.000000	0.011364	0.000000	0.000000	0.011364	0.000000	0.0000	0.0000	0.00000	0.011364	0.011364	0.0000	0.0000	0.0000	0.011364	0.000	0.0000	0.000000	0.00000	0.000	0.034091	0.00000	0.022727	0.011364	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.0000	0.0000	0.022727	0.00000	0.000000	0.000000	0.000000	0.034091	0.011364	0.000000	0.000000	0.000000	0.00000	0.0000	0.0000	0.00000	0.000000	0.0000	0.000000	0.000000	0.00000	0.00000	0.000000	0.011364	0.0000	0.000000	0.00000	0.00000	0.000000	0.056818	0.000000	0.000000	0.000000	0.011364	0.0000	0.0000	0.0000	0.000000	0.000000	0.0000	0.011364	0.00000	0.000000	0.000000	0.0000	0.011364	0.000000	0.000000	0.00000	0.0000	0.00000	0.000000	0.000000	0.0000	0.00000	0.011364	0.011364	0.011364	0.000000	0.000000	0.011364	0.011364	0.011364	0.034091	0.000	0.022727	0.000000	0.000000	0.011364	0.011364	0.00000	0.0000	0.000000	0.000000	0.0000	0.000	0.000000	0.250000	0.000000	0.034091	0.000000	0.000000	0.000000	0.000000	0.000000	0.0000	0.045455	0.0000	0.000000	0.011364	0.0000	0.011364	0.011364	0.00000	0.0000	0.022727	0.000000	0.011364	0.000000	0.000000	0.00000	0.00000	0.000000	0.000000	0.011364	0.011364
306	Agincourt_Toronto	0.000000	0.0000	0.0125	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.025000	0.025	0.0125	0.175000	0.0125	0.000000	0.000000	0.000000	0.000000	0.0000	0.000000	0.0000	0.000000	0.000000	0.000000	0.012500	0.000000	0.000000	0.000000	0.025	0.000000	0.000000	0.012500	0.000000	0.0250	0.012500	0.037500	0.0125	0.000	0.037500	0.0125	0.00000	0.000000	0.000000	0.012500	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.012500	0.0000	0.0000	0.00000	0.000000	0.000000	0.0000	0.0000	0.0125	0.025000	0.025	0.0000	0.000000	0.00000	0.000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.025000	0.025000	0.0000	0.0000	0.025000	0.00000	0.000000	0.012500	0.012500	0.012500	0.000000	0.025000	0.000000	0.000000	0.00000	0.0000	0.0000	0.00000	0.000000	0.0000	0.000000	0.000000	0.00000	0.00000	0.012500	0.000000	0.0000	0.012500	0.00000	0.01250	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0000	0.0000	0.0125	0.012500	0.000000	0.0125	0.000000	0.00000	0.000000	0.000000	0.0000	0.012500	0.000000	0.000000	0.00000	0.0125	0.00000	0.000000	0.000000	0.0125	0.01250	0.000000	0.000000	0.012500	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000	0.000000	0.000000	0.000000	0.000000	0.000000	0.03750	0.0125	0.000000	0.000000	0.0125	0.025	0.000000	0.000000	0.000000	0.012500	0.012500	0.000000	0.000000	0.000000	0.000000	0.0000	0.000000	0.0125	0.000000	0.037500	0.0000	0.000000	0.000000	0.00000	0.0000	0.000000	0.012500	0.000000	0.000000	0.000000	0.00000	0.01250	0.000000	0.000000	0.000000	0.000000
307	Alderwood, Long Branch_Toronto	0.000000	0.0000	0.0000	0.000000	0.012346	0.012346	0.012346	0.012346	0.00000	0.000000	0.012346	0.000	0.0000	0.012346	0.0000	0.000000	0.000000	0.037037	0.000000	0.0000	0.024691	0.0000	0.000000	0.012346	0.012346	0.012346	0.000000	0.012346	0.012346	0.000	0.024691	0.000000	0.000000	0.000000	0.0000	0.000000	0.000000	0.0000	0.000	0.012346	0.0000	0.00000	0.037037	0.000000	0.037037	0.000000	0.012346	0.012346	0.037037	0.012346	0.037037	0.012346	0.012346	0.012346	0.0000	0.0000	0.00000	0.000000	0.000000	0.0000	0.0000	0.0000	0.000000	0.000	0.0000	0.000000	0.00000	0.000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.012346	0.037037	0.0000	0.0000	0.012346	0.00000	0.000000	0.000000	0.000000	0.012346	0.012346	0.012346	0.000000	0.000000	0.00000	0.0000	0.0000	0.00000	0.000000	0.0000	0.012346	0.000000	0.00000	0.00000	0.000000	0.000000	0.0000	0.000000	0.00000	0.00000	0.012346	0.012346	0.012346	0.012346	0.012346	0.000000	0.0000	0.0000	0.0000	0.037037	0.000000	0.0000	0.000000	0.00000	0.000000	0.000000	0.0000	0.012346	0.000000	0.000000	0.00000	0.0000	0.00000	0.012346	0.012346	0.0000	0.00000	0.000000	0.000000	0.012346	0.000000	0.000000	0.000000	0.000000	0.000000	0.012346	0.000	0.024691	0.000000	0.012346	0.000000	0.012346	0.00000	0.0000	0.012346	0.024691	0.0000	0.000	0.012346	0.000000	0.000000	0.037037	0.012346	0.012346	0.012346	0.012346	0.000000	0.0000	0.037037	0.0000	0.012346	0.000000	0.0000	0.000000	0.000000	0.00000	0.0000	0.000000	0.012346	0.000000	0.012346	0.000000	0.00000	0.00000	0.000000	0.012346	0.000000	0.000000
308	Bathurst Manor, Wilson Heights, Downsview Nort...	0.000000	0.0000	0.0000	0.012195	0.000000	0.000000	0.000000	0.000000	0.00000	0.012195	0.000000	0.000	0.0000	0.012195	0.0000	0.000000	0.000000	0.048780	0.012195	0.0000	0.000000	0.0000	0.000000	0.000000	0.000000	0.000000	0.012195	0.000000	0.000000	0.000	0.000000	0.000000	0.012195	0.012195	0.0000	0.000000	0.012195	0.0000	0.000	0.024390	0.0000	0.00000	0.000000	0.000000	0.024390	0.012195	0.000000	0.000000	0.000000	0.012195	0.012195	0.000000	0.012195	0.085366	0.0000	0.0000	0.00000	0.000000	0.000000	0.0000	0.0000	0.0000	0.000000	0.000	0.0000	0.000000	0.00000	0.000	0.000000	0.00000	0.000000	0.000000	0.000000	0.012195	0.012195	0.00000	0.000000	0.012195	0.0000	0.0000	0.000000	0.00000	0.012195	0.012195	0.012195	0.000000	0.000000	0.000000	0.000000	0.012195	0.00000	0.0000	0.0000	0.00000	0.012195	0.0000	0.000000	0.036585	0.00000	0.00000	0.012195	0.000000	0.0000	0.012195	0.00000	0.00000	0.000000	0.036585	0.000000	0.000000	0.000000	0.000000	0.0000	0.0000	0.0000	0.060976	0.012195	0.0000	0.000000	0.00000	0.000000	0.012195	0.0000	0.012195	0.012195	0.012195	0.00000	0.0000	0.00000	0.012195	0.000000	0.0000	0.00000	0.000000	0.000000	0.012195	0.000000	0.012195	0.000000	0.000000	0.012195	0.012195	0.000	0.012195	0.000000	0.012195	0.000000	0.000000	0.00000	0.0000	0.000000	0.000000	0.0000	0.000	0.000000	0.060976	0.012195	0.036585	0.012195	0.000000	0.000000	0.000000	0.012195	0.0000	0.036585	0.0000	0.000000	0.000000	0.0000	0.012195	0.012195	0.04878	0.0000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.00000	0.012195	0.000000	0.000000	0.012195

	Neighborhood	1st Most Common Category	2nd Most Common Category	3rd Most Common Category	4th Most Common Category	5th Most Common Category	6th Most Common Category	7th Most Common Category
0	Allerton_NYC	Salon / Barbershop	Laundry Service	Food	Gas Station	Pizza Place	Non-Profit	Car Wash
1	Annadale_NYC	Pizza Place	Salon / Barbershop	Nail Salon	Tattoo Parlor	American Restaurant	Pet Store	Bagel Shop
2	Arden Heights_NYC	Professional & Other Places	Doctor's Office	Church	Dentist's Office	Bar	Gym	General Entertainment
3	Arlington_NYC	Church	Hardware Store	Salon / Barbershop	Professional & Other Places	Residential Building (Apartment / Condo)	Automotive Shop	American Restaurant
4	Arrochar_NYC	Deli / Bodega	Pizza Place	Food Truck	Dry Cleaner	Dance Studio	Doctor's Office	Bar

	Cluster Labels	1st Most Common Category	2nd Most Common Category	3rd Most Common Category	4th Most Common Category	5th Most Common Category	6th Most Common Category	7th Most Common Category
Neighborhood
Wingate_NYC	3	Salon / Barbershop	School	Deli / Bodega	Caribbean Restaurant	Event Space	Food	Residential Building (Apartment / Condo)
Woodhaven_NYC	3	Deli / Bodega	Salon / Barbershop	Laundry Service	Miscellaneous Shop	Liquor Store	Doctor's Office	Chinese Restaurant
Woodlawn_NYC	3	Bar	Deli / Bodega	Salon / Barbershop	Pub	Church	Food & Drink Shop	Pizza Place
Woodrow_NYC	4	Pool	Grocery Store	School	Physical Therapist	Dentist's Office	Salon / Barbershop	Bar
Woodside_NYC	3	Bar	Salon / Barbershop	Mexican Restaurant	Thai Restaurant	Platform	Deli / Bodega	Miscellaneous Shop
Yorkville_NYC	2	Residential Building (Apartment / Condo)	Laundry Service	Spa	Pharmacy	Flower Shop	Gym	Salon / Barbershop
Agincourt_Toronto	0	Automotive Shop	Storage Facility	Post Office	Church	Coffee Shop	Gas Station	Business Service
Alderwood, Long Branch_Toronto	4	Convenience Store	Salon / Barbershop	Spa	Dentist's Office	Gas Station	Daycare	Bank
Bathurst Manor, Wilson Heights, Downsview North_Toronto	1	Doctor's Office	Residential Building (Apartment / Condo)	Medical Center	Synagogue	Bank	Spa	Ice Cream Shop
Bayview Village_Toronto	4	Salon / Barbershop	Doctor's Office	Church	Dog Run	Bank	Grocery Store	Japanese Restaurant

	Cluster Labels	1st Most Common Category	2nd Most Common Category	3rd Most Common Category	4th Most Common Category	5th Most Common Category	6th Most Common Category	7th Most Common Category
Neighborhood
Bloomfield_NYC	1	Doctor's Office	Automotive Shop	Baseball Field	Grocery Store	Sporting Goods Shop	Other Great Outdoors	Cosmetics Shop
Brooklyn Heights_NYC	1	Doctor's Office	Residential Building (Apartment / Condo)	School	Salon / Barbershop	Women's Store	Church	Laundry Service
Bulls Head_NYC	1	Doctor's Office	Pizza Place	Bank	Church	Dentist's Office	Hospital	Salon / Barbershop
Castleton Corners_NYC	1	Doctor's Office	Church	Medical Center	Salon / Barbershop	Dentist's Office	Deli / Bodega	Nail Salon
Concord_NYC	1	Doctor's Office	Deli / Bodega	Chinese Restaurant	Medical Center	Martial Arts School	Bakery	Automotive Shop
Dongan Hills_NYC	1	Deli / Bodega	Doctor's Office	Salon / Barbershop	Church	Bar	Pet Store	Residential Building (Apartment / Condo)
Dyker Heights_NYC	1	Doctor's Office	Gas Station	Salon / Barbershop	Dog Run	Automotive Shop	Deli / Bodega	Event Space
Egbertville_NYC	1	Doctor's Office	Salon / Barbershop	Park	Automotive Shop	Dentist's Office	Trail	Medical Center
Emerson Hill_NYC	1	Doctor's Office	Medical Center	Residential Building (Apartment / Condo)	Church	College Library	Spa	Grocery Store
Forest Hills_NYC	1	Dentist's Office	Doctor's Office	Residential Building (Apartment / Condo)	Convention Center	School	Library	Elementary School
Fort Hamilton_NYC	1	Doctor's Office	Residential Building (Apartment / Condo)	Pizza Place	Deli / Bodega	Salon / Barbershop	Laundry Service	Miscellaneous Shop
Georgetown_NYC	1	Doctor's Office	Bank	Chinese Restaurant	Medical Center	Discount Store	Salon / Barbershop	Laundry Service
Glendale_NYC	1	Doctor's Office	Deli / Bodega	Chinese Restaurant	Salon / Barbershop	Church	School	Dentist's Office
Gramercy_NYC	1	Doctor's Office	Residential Building (Apartment / Condo)	Medical Center	Salon / Barbershop	Dentist's Office	Laundry Service	Ice Cream Shop
Greenridge_NYC	1	Doctor's Office	Professional & Other Places	Dentist's Office	Asian Restaurant	Laundry Service	Parking	Taxi
Heartland Village_NYC	1	Doctor's Office	Bagel Shop	Chinese Restaurant	Professional & Other Places	Pizza Place	Dentist's Office	Salon / Barbershop
Holliswood_NYC	1	Doctor's Office	Residential Building (Apartment / Condo)	Gas Station	Salon / Barbershop	Dentist's Office	Caribbean Restaurant	Event Space
Kew Gardens_NYC	1	Residential Building (Apartment / Condo)	Doctor's Office	Synagogue	Laundry Service	Taxi	Dentist's Office	Other Great Outdoors
Lindenwood_NYC	1	Doctor's Office	Dentist's Office	Residential Building (Apartment / Condo)	Bank	Medical Center	Deli / Bodega	Miscellaneous Shop
Madison_NYC	1	Doctor's Office	Synagogue	Residential Building (Apartment / Condo)	Dentist's Office	Eastern European Restaurant	Medical Center	Food
Manhattan Terrace_NYC	1	Doctor's Office	Residential Building (Apartment / Condo)	Dentist's Office	Housing Development	Parking	School	Medical Center
Morris Park_NYC	1	Doctor's Office	Residential Building (Apartment / Condo)	Dentist's Office	Nail Salon	Ice Cream Shop	Bank	Pizza Place
Murray Hill, Manhattan_NYC	1	Doctor's Office	Residential Building (Apartment / Condo)	Dentist's Office	Hotel	Parking	Embassy / Consulate	Event Space
New Springville_NYC	1	Doctor's Office	Deli / Bodega	Salon / Barbershop	Bagel Shop	Mobile Phone Shop	Chinese Restaurant	Pizza Place
Norwood_NYC	1	Doctor's Office	Park	Residential Building (Apartment / Condo)	Deli / Bodega	Medical Center	Salon / Barbershop	Food Truck
Oakwood_NYC	1	Doctor's Office	Dentist's Office	Church	Medical Center	Elementary School	Other Nightlife	Other Great Outdoors
Ocean Parkway_NYC	1	Residential Building (Apartment / Condo)	Doctor's Office	Assisted Living	Dentist's Office	Coworking Space	Synagogue	Pizza Place
Park Hill_NYC	1	Residential Building (Apartment / Condo)	Doctor's Office	Salon / Barbershop	Athletics & Sports	Professional & Other Places	Park	Other Great Outdoors
Pelham Gardens_NYC	1	Doctor's Office	Pizza Place	Pharmacy	Playground	Other Great Outdoors	Dentist's Office	Garden
Pelham Parkway_NYC	1	Doctor's Office	Dentist's Office	Residential Building (Apartment / Condo)	Deli / Bodega	Diner	Pizza Place	Salon / Barbershop
Pomonok_NYC	1	Doctor's Office	Residential Building (Apartment / Condo)	Playground	Dentist's Office	Elementary School	Salon / Barbershop	Medical Center
Randall Manor_NYC	1	Doctor's Office	High School	Residential Building (Apartment / Condo)	Park	College Classroom	Medical Center	Deli / Bodega
Rego Park_NYC	1	Doctor's Office	Residential Building (Apartment / Condo)	Medical Center	Dentist's Office	Professional & Other Places	Mediterranean Restaurant	General College & University
Rockaway Park_NYC	1	Beach	Doctor's Office	Eye Doctor	Bakery	Restaurant	Track	Medical Center
Schuylerville_NYC	1	Doctor's Office	Salon / Barbershop	Deli / Bodega	Professional & Other Places	Pizza Place	Pharmacy	Mental Health Office
South Beach_NYC	1	Hospital	Medical Center	Doctor's Office	Beach	Dentist's Office	Deli / Bodega	College Communications Building
Starrett City_NYC	1	Doctor's Office	Laundry Service	Residential Building (Apartment / Condo)	Parking	Pharmacy	Salon / Barbershop	Medical Center
Sunnyside, Staten Island_NYC	1	Doctor's Office	Theater	College Residence Hall	College Library	Dance Studio	College Administrative Building	College Arts Building
Todt Hill_NYC	1	Doctor's Office	Professional & Other Places	Nail Salon	School	Laundry Service	Bagel Shop	Dog Run
Tudor City_NYC	1	Doctor's Office	Residential Building (Apartment / Condo)	Medical Center	Gym	Laundry Service	Hospital	Assisted Living
Upper East Side_NYC	1	Doctor's Office	Art Gallery	Residential Building (Apartment / Condo)	Medical Center	Dentist's Office	Library	Spa
Bathurst Manor, Wilson Heights, Downsview North_Toronto	1	Doctor's Office	Residential Building (Apartment / Condo)	Medical Center	Synagogue	Bank	Spa	Ice Cream Shop
Summerhill West, Rathnelly, South Hill, Forest Hill SE, Deer Park_Toronto	1	Residential Building (Apartment / Condo)	Doctor's Office	Dentist's Office	Medical Center	Taxi	Light Rail Station	Athletics & Sports

	Cluster Labels	1st Most Common Category	2nd Most Common Category	3rd Most Common Category	4th Most Common Category	5th Most Common Category	6th Most Common Category	7th Most Common Category
Neighborhood
The Annex, North Midtown, Yorkville_Toronto	2	Residential Building (Apartment / Condo)	Bed & Breakfast	Japanese Restaurant	Metro Station	Playground	Miscellaneous Shop	Sandwich Place
Victoria Village_Toronto	2	Residential Building (Apartment / Condo)	Automotive Shop	Grocery Store	Government Building	Auto Dealership	Playground	Transportation Service
Willowdale, Willowdale West_Toronto	2	Residential Building (Apartment / Condo)	Park	Medical Center	Bank	Synagogue	Pizza Place	Financial or Legal Service
York Mills West_Toronto	2	Residential Building (Apartment / Condo)	Gym	Park	Medical Center	Church	School	Dentist's Office
York Mills, Silver Hills_Toronto	2	Residential Building (Apartment / Condo)	Park	High School	Church	Pool	School	Synagogue

	% of venues
Category
Automotive Shop	13.9286
Church	2.78571
Gas Station	2.5
Salon / Barbershop	2.21429
Factory	2
Deli / Bodega	1.92857
Pizza Place	1.85714

	% of venues
Category
Doctor's Office	13.5384
Residential Building (Apartment / Condo)	5.23145
Dentist's Office	3.8364
Medical Center	2.88523
Salon / Barbershop	2.40964
Deli / Bodega	2.34623
Laundry Service	1.6487

	% of venues
Category
Residential Building (Apartment / Condo)	14.2916
Salon / Barbershop	3.28209
Deli / Bodega	2.61737
Laundry Service	2.30577
Doctor's Office	2.285
Church	1.72414
Park	1.51641

	% of venues
Category
Salon / Barbershop	7.343
Deli / Bodega	3.98551
Pizza Place	2.47585
Church	2.35507
Laundry Service	2.29469
Chinese Restaurant	2.18599
Residential Building (Apartment / Condo)	2.04106

	% of venues
Category
Residential Building (Apartment / Condo)	2.3309
Salon / Barbershop	2.0878
Church	1.7446
Doctor's Office	1.7303
Park	1.6016
Pizza Place	1.5873
Deli / Bodega	1.4014

IBM Coursera Capstone Project¶

Table of Contents¶

Introduction: Business Problem¶

Data¶

Methodology and Results¶

0. Importing relevant modules and plotting configurations¶

1. Data for New York City¶

1.1 Coordinates of neighborhoods in NYC¶

Downloading and saving neighborhood data of New York City¶

Loading the saved neighborhood JSON data for New York City¶

Saving the neighborhood data into a pandas dataframe¶

Transforming data further (needed later for the venue data)¶

Using geopy Library to get the Coordinates of New York City¶

Creating a Map of New York City with neighborhoods Superimposed on Top¶

1.2 NYC Venue Data (Latitude, Longitude, Category)¶

Downloading and saving Venue Data For New York City from Foursquare¶

Loading the saved data into a pandas dataframe¶

Let's check how many venues were returned for each neighborhood¶

Let's find out how many unique categories can be curated from all the returned venues¶

Performing one-hot encoding on the Venue Category variable¶

Grouping rows by neighborhood and taking the mean of the frequency of occurrence of each category for each neighborhood¶

The most common categories for each neighborhood¶

1.3 Clustering¶

Creating a map that shows the neighborhoods and their clusters¶

Examining clusters¶

2. Data for Toronto¶

2.1 Coordinates of neighborhoods in Toronto¶

Downloading and saving neighborhoods Data for Toronto¶

Removing records where the borough is "not assigned"¶

Merging records where multiple neighborhoods share the same borough¶

Getting Latitude and Longitude Coordinates of the Neighborhoods¶

Adding latitude and longitude data to the neighborhood dataframe¶

Using geopy Library to get the coordinates of Toronto¶

Creating a map of Toronto with neighborhoods superimposed on top¶

2.2 Toronto Venue Data (Latitude, Longitude, Category)¶

Downloading and saving Venue Data For Toronto from Foursquare¶

Loading the saved data into a pandas dataframe¶

Let's check how many venues were returned for each neighborhood¶

Let's find out how many unique categories can be curated from all the returned venues¶

Performing one-hot encoding on the Venue Category variable¶

Grouping rows by neighborhood and taking the mean of the frequency of occurrence of each category for each neighborhood¶

The most common categories for each neighborhood¶

2.3 Clustering¶

Creating a map that shows the neighborhoods and their clusters¶

Examining clusters¶

3. Exploratory Data Analysis¶

Top 10 Common Venue Categories in NYC¶

Top 10 Common Venue Categories in Toronto¶

Top 10 Venue Categories that Exist in Most NYC Neighborhoods¶

Top 10 Venue Categories that Exist in Most Toronto Neighborhoods¶

Rare Venue Categories in NYC¶

Rare Venue Categories in Toronto¶

Combining NYC and Toronto data¶

Top 10 Common Venue Categories in both NYC and Toronto¶

Rare Categories in both NYC and Toronto¶

Clustering¶

Examining clusters¶

Cluster 1¶

Cluster 2¶

Cluster 3¶

Cluster 4¶

Cluster 5¶

Cluster 1:

Cluster 2:

Cluster 3:

Cluster 4:

Cluster 5:

Discussion¶

Conclusion¶

Using `geopy` Library to get the Coordinates of New York City¶

Performing one-hot encoding on the `Venue Category` variable¶

Using `geopy` Library to get the coordinates of Toronto¶

Performing one-hot encoding on the `Venue Category` variable¶